{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets\n",
    "This tutorial is to demonstrate an example of using PRUDEX-Compass to visualize the test result for portfolio management.\n",
    "\n",
    "A more detailed tutorial could be found [here](https://github.com/ai-gamer/PRUDEX-Compass)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up experinment environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): \\ ^C\n",
      "failed\n",
      "\n",
      "CondaError: KeyboardInterrupt\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from IPython.display import clear_output\n",
    "import argparse\n",
    "import sys\n",
    "import numpy as np\n",
    "import torch\n",
    "from torch import nn\n",
    "import yaml\n",
    "import os\n",
    "import pandas as pd\n",
    "module_path = os.path.abspath(os.path.join('..'))\n",
    "sys.path.append(module_path)\n",
    "requirements_path=module_path+\"/requirements.txt\"\n",
    "print(requirements_path)\n",
    "command=\"pip install -r \"+requirements_path\n",
    "os.system(command)\n",
    "clear_output(wait=True)\n",
    "! conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch\n",
    "clear_output(wait=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is often the case that we run several random seeds on different datasets during different time periods for one specific algorithm.\n",
    "\n",
    "However, it is sometimes difficult to evaluate the results and compare different algorithms when so much data emerges. \n",
    "\n",
    "Therefore, we introduce PRUDEX-Compass for a systematic evaluation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance Profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from visualization.performance_profile import performance_profile\n",
    "def load(path:str):\n",
    "    with open(path, 'r', encoding='utf-8') as f:\n",
    "        dict = eval(f.read())  \n",
    "    return dict\n",
    "dict_algorithm=load(\"visualization_data/pp.txt\")\n",
    "for key in dict_algorithm:\n",
    "    dict_algorithm[key]=np.array(dict_algorithm[key])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "algorithms= ['A2C','DeepTrader','PPO',\"EIIE\",'SAC',\"IMIT\",'SARL',\"AlphaMix+\"]\n",
    "reps=2000\n",
    "xlabel=r'total return score $(\\tau)$'\n",
    "dic='result/visualization/pp.pdf'\n",
    "color=['moccasin','aquamarine','#dbc2ec','orchid','lightskyblue','lightslategrey','orange',\"lightcoral\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DJk6DwWAwGAyGyWGEBoPBYDAYDFVhhAaDwWAwGAxVYYQGg8FgMBgMVWGEBoPBYDAYDFVhhAaDwWAwGAxVYYQGg8FgMBgMVWGEBoPBYDAYDFVhhAaDwWAwGAxVYYQGg8FgMBgMVXFQCA1f+cpXZruue7zruscfdthhxy9fvvzYyy677PDrr79+zrZt2/ZK/o077rijtdSn0f7OOOOMESlfJ0NfX590Xff4O+64o3X82oapoPzdG/73s5/9rAXAdd3jf/CDH0wrnfORj3xk4aWXXnp46fMjjzxSP1ob//iP/7hgb9zX/s5XvvKV2SeccMIygHXr1kVKz/Oxxx6rG173xhtvnDX891g655577mkc6/sp/9uT93egcMYZZxztuu7xq1atipYfL42ffX19Nc1h5d/7ZCn9ts8+++yjKpWfffbZR7mue/xXvvKV2aVjw3/b+yMHfMKqEolEIvje9763CqC3t9d6+eWXE7/85S+n3X333W3f//73V51wwgl7NIPZxRdf3HPooYe+Vvr829/+tvnnP//5jNtvv33gWDQarZjW1bB/Uf7ulbNkyZJ8Le188YtfXLdgwYIh50yfPt2bbP8MIbFYTK1YsaLljDPO6C8//uCDD7bEYrFRf4vHHXdcuvx3++yzz9Z997vfnfvVr351zYwZM8z3M0GefPLJZHt7ewTgl7/8ZcunP/3pbXu7T8OJRCJ6x44d0WeffTZRngXzmWeeSbS3t0cikciQ5E4f+9jHtmWzWbHnezp1HDRCg2VZ+pRTThnIdHnRRRf1vu9979t55ZVXutdee+0hDz300Mu2vecex/Tp0/1Sum2AF154IQFQ3sfhZDIZkUgkxs4wtgfwfZ8gCEQ0Gt3rfdkfGP7uTZQjjzwyc/TRR+emok+GkZxyyindf/jDH5p9399YGgtefPHF+ObNm2NnnnnmrpUrV47QQgA0Njaq8u+3s7PTBli2bFlm0aJFhT3S+QOQu+++uyUWi6kFCxZkH3zwwX1SaIhGo8FRRx2Vufvuu1vKhYa77767ZdmyZX2rVq1KlNevdaFQiXXr1kUuvvjio++///6X9sb7dVBsT4xGc3Nz8PGPf3zztm3bog899FADQDabFV/4whfmnnbaaccceeSRyy+66KIjfvvb3zYOP/dHP/pR2wUXXHDkkUceufz0008/+hvf+MaM8vKSGuo3v/lN07nnnnvkUUcdtfwtb3mL+8orr8Sq7Z/rusd/+9vfnvHZz3523oknnrjsjW9845EA9957b+OVV1556EknnbTs2GOPPe6KK6447IEHHmgYfv4vf/nLpnPOOeeoo48+evnb3vY297XXXqt47Vru5YILLjjymGOOWf7HP/4xWe19GAz7A+ecc053NpuVjzzySH3p2G9+85uWI444ot9odPYsvu/z8MMPt5xyyindb3rTmzo2bdoU+8tf/hIfrX5pu+i///u/W/7+7/9+0bJly4476aSTlv3bv/3brEr1n3/++fgVV1xx2NFHH33cJZdccsSjjz46RCC88847W9/ylre4xx9//LHLly8/9q//+q+XPvvss4lKbV188cW7/u///q9ZqVAZpZTi4Ycfbrn44ot3Da87fHvi3e9+95Jzzz33qEwmM6B9+Na3vjXjqKOOWv7SSy9VPVfsSQ5qoQHgrLPO6rMsS//5z3+uA/jgBz+4+P7772/9u7/7u23f+MY3Vh9++OHpT37yk0v+/Oc/D7ywN99884yvfe1r888888zum2++efVb3vKWnbfddtuc8n1pgPb29shNN9007wMf+MC266+/fm06nbY++MEPLq1FPfXTn/50RkdHh3P99dev+9SnPrUJYNOmTZEzzzyz5/rrr1/3ta99bc3RRx/d/7GPfezQxx9/fGAif+655xKf+9znFi9evDhz4403rj7jjDO6P/nJTy4e3n6197Jjx47IzTffPPe9733vtm9961urFi1aNGmJ+WDC87wRf7USBIEY3kZpoDJMnmQyqU4++eSeFStWtJSOPfTQQ82VBn/D7uWRRx6p7+7uti+99NJdb37zm7ts29a//vWvW8Y775vf/ObcWCymbrzxxjWXX355x+233z77lltuGTKW5fN5+ZnPfGbR2972tp033njjGsdx1Cc/+cnF6XR6YD7cvHlz5LLLLuu88cYb19xwww1rp0+fXnjf+9532Jo1ayLDr3nFFVd0d3d3O48//ngdwGOPPVbX09Njv+lNb+oer79f/epXN/T29tpf+tKX5gKsXLky9sMf/nDO+973vq37qlZxQvr4H7FuyQYyI1bfe4IFJHrey6LVU9VePB7XDQ0Nfmdnp/373/++/plnnmm85ZZbUmeffXY/wIUXXtj7tre9Lfbd73531m233ba2p6dH/uhHP5r9nve8Z9s///M/bwO44IILenO5nPzRj340++qrr95ZUm329vbaN99882unnXZaGuC4447LXHLJJUffeeedbR/4wAd2VtO/lpYW79Zbb11bfuxDH/rQwLlBEHD22Wf3rl27Nv7zn/982umnn54G+MEPfjBzzpw5uVtvvXWtlJJLLrmk1/M8cdttt80pnVvLvfT19dk//OEPXz/uuOOyE3/ak2fzdzYsya7ZO+9efHGiZ+4/LKj53evr67OPOuqoEYZwtaoX3/GOdxwx/NjnPve59e95z3s6a+3TVHPLz1YsWbdp2175XhbNm9VzzTsvn5Ix4Y1vfOOu66+/fmE+n9/4l7/8Jb5z587Im9/85q5vfetbM6ei/b1B/+23Lwk2bNgr3421YEFP3VVX1fzdrFixoiWZTAYXXnhhbzQa1cuXL+/9/e9/3/KFL3xhi5Sjr3UXLlyY/fd///cNEG5B79q1y7799ttnXX311TstywKgUCjIT3/605vOPffcPoCZM2d6V1555RGPPfZY3cUXX9wLUBoPIRxjzz///N6LLroo+Ytf/KK1vAxCjfUJJ5zQs2LFipYzzzyz/5577mk54YQTepqamoLx7nP27NneP/3TP2287rrrFp133nndN99889zFixdnPvrRj24v1VFKEQSDTQVBIEr/li8+HMcZ73JTwkFj0zAWWmsB8Pjjjzc0Nzd7p512Wn/5l3HiiSf23nvvvW0ATz/9dF0ul5OXX355V3md0047re+OO+6YtXnz5sjChQsLAI2NjX5JYABYuHBhYcmSJemXXnopCVQlNJxyyik9w49t2rTJ+epXvzrnueeea+jq6nK0Dk0LjjjiiAEDrtdeey153nnn7Sr/gV166aXd5UJDLffS2trq7W2BYX8lkUgEt9566+vDj8+ePbsmdcP111+/driGx2h8ppaLL76454tf/CIPPPBAw9NPP11/7LHH9k2bNs0f/0zDVJHP58Vjjz3WfPrpp3eX7KYuueSSXV/84hcXPfXUU8nyMXU455xzTnf55wsvvLDrt7/9bdvmzZsjCxYsKADYtq3POuusvlKdI444Igewbdu2AS3CypUrY1/72tfmvPLKK3U9PT0D8+TGjRsrbhm88Y1v7Lrpppvm5XK5TX/4wx+aP/nJT26s9n7f8Y537HrwwQebPvaxjx0qpdR33XXXynL7ukcffbT+mmuuWTr8vEsvvXSI18aesnGYkNAwlSv9vU02mxV9fX1Wa2ur397e7nR1dTmVVoWlyXfXrl02wFvf+tYjK7W3adOmgYm2qalpxKTQ3Nzsd3Z2Vi0StrW1DRmwgiDgmmuuWZLJZKxrrrlm68KFC3PJZFJ985vfnN3V1TXQbldXl9Pa2jrk3OH7spO9l73BRFb6exvLsnS5kdREOfzww7P7qspyqlb6e5tYLKZPO+207nvuuaflpZdeqv/Qhz60ZW/3abJMZKW/N/nd737XmE6nrbPOOqunq6vLAjjjjDP6HMfRd999d8tYQkNbW9uQcaok8G3bts0pCQ3xeDwoaR0ASoJJPp8XAL29vfIDH/jA0qamJu/jH//4prlz5xZisZj6/Oc/v7BUZziXXnpp9w033LDghhtumJPL5eSll146YrE3Fpdeeumuxx57rPnkk0/uWbp06ZCFwPLly9N33nnnq6XP27dvd6699tolX//611fPnDlz4H5rXYRMlINe0/DII4/UB0Egli9f3v/kk0/Wt7a2et/85jdH/ZE1NTX5AP/+7/++upJxlOu6A4N6d3f3COGgq6vLXrRoUdUDvxBiiIfC6tWro2vWrEl885vfXFVSpUG4T1der7m52StZcZdob28f0p9a7kWI/dpLyGComksvvXTXJz7xiUMty9JXXHFF997uz8HGPffc0wLw2c9+9pDhZY888kiL7/ubRju3o6NjyBi3c+dOG2DWrFlVT6hPP/10XUdHh3Pbbbe9fvjhhw+Mgel02hrtnLq6OnXyySf33HXXXTPOOOOMrrq6uqqNjXp6euRNN90075BDDsk+/fTTTb/73e8aLrroooGxvaGhQZUvOtatWxcBOOqoo7J7w3vioBYaurq6rJtvvnnurFmz8uedd16vEIKf//znM+vq6lT5y1LOySefnI5EImrHjh3OeNJkT0+P/cQTTwyo0zZs2BBZs2ZN4oorrpjwHnQ2m5UwKB0DrF+/PrJy5cq6hQsXDmwfuK6bfvzxx5uUUgN7gPfee2/TRO/FYDhYOP/883tPP/30roULF+aq2Zc2TB39/f3y6aefbjznnHN2XXnllR3lZS+99FL8O9/5zryHH354hKdYiYcffrjpgx/84MDW7wMPPNDc3NzszZ07t+rJNZfLlcbYgYn/iSeeSLa3t0eWLl06qpbjne98585CoSDe+c53VrX1XOLzn//8fKWU+NnPfpb67Gc/O/9f//VfF77hDW94ZV999w4aoSEIAvHUU08lAfr7+60XX3wx8atf/WpaPp+X3//+91fZts3555/fe/zxx/dcffXVS9/97ndvc10319fXZ61cuTKez+flddddt6W5uTl43/vet/Ub3/jGvC1btkTe8IY39CulWLt2bezZZ5+t/9GPfrSmdM2Ghgb/M5/5zCF///d/vyUWi6nvf//7sxsbG/13vetdHaP3dGwOO+ywXFtbm/e1r31tbiaT2drf3y9vueWWOS0tLUMk6WuuuWb7e97znsM/+MEPHvJXf/VXHalUKr5ixYohVsS13Ith4pS/e+XMmzevMHfu3KpXQK+88kqiv79/yGqnsbExKO3JGqYGx3H44Q9/uHb8moap5p577mnK5/Pyqquuaj/55JOHTNCnnnpq/49//ONZK1asaDnhhBP6Kp2/fv36+Cc+8YkFF198cdcf//jHuvvuu6/tYx/72Kby7YjxOOmkk/pjsZj6zGc+s/D973//9m3btjm33nrr7NbW1jF/q2effXbf2WefXbFfo3HPPfc0PvDAA60333zzqubm5uBLX/rSxksvvfTIz3/+8/O+/e1vr6+lrT3FQSM0ZDIZ66qrrjpMCEE8Hg9mz56dv+iii3ZdffXVO2bNmuVDaLdw2223rfn3f//3Wf/93/89Y+fOnZG6urpg8eLFmb/9279tL7X18Y9/fMeMGTO8O++8c8bPf/7zmY7jqDlz5uQuvPDCrvJrTp8+vXD11Vdv+9a3vjW3vb09cuihh2a+/vWvvx6PxyccFCkWi+lvfOMbq6+//voF//zP/7y4tbW18L73vW/bs88+W7927doBt9ATTzwxc/3116/9zne+M+faa69dcuihh6a//vWvr3n3u989JIRptfdimDild2/48fe///1bP/WpT1UdsOa6665bNPzYscce2/c///M/I4wsDYb9kd/+9rcts2fPzg8XGCCMvnjOOed0PfTQQy3HHHNMf6XzP/rRj25+5JFHGv/pn/5pcSQSUe95z3u2XXPNNe2V6o7GzJkz/a9+9atrbrrppnmf/OQnl8yePTv3uc99buN//ud/TqkHTWdnp/WlL31pwSWXXNJR2mpubW0NPv/5z2/4xCc+sWTFihVdl19++T6nARYly/vReOGFF9YvW7Zswivjg5WPfOQjC9euXRu/9957Xx2/tsFgMBgmSilK4k033bT6sssu2+cm2n2BF154oW3ZsmULJ9vOQR/cyWAwGAwGQ3UYocFgMBgMBkNVHDQ2DXuafdWIxWAwGA40Fi1aVEilUs/t7X4cDBhNg8FgMBgMhqqoRmhQSikT2cdgMBgMhv2Q4hw+JdntxhUahBDbs9nsPpmi02AwGAwGw9hks9mYEGL7+DXHZ1yhwff9L65fvz6STqfjRuNgMBgMBsP+gVJKpNPp+Pr16yO+739xKtocN04DwPPPP3+RbdvXaa1nYuwgDAaDwWDYH1BCiO2+739x+fLlv5uKBqsSGgwGg8FgMBiM1sBgMBgMBkNVGKHBYDAYDAZDVRihwWAwGAwGQ1UYocFgMBgMBkNVVC00uK77A9d14+PX3PdxXVe7rmssQA0Gg8FgqIFack98APgCkN1NfdlX2a3CRT6fJxqN7s5LHPCYZzg5zPObHOb5TR7zDCfHbnh+o8ZkqmV7omIjrus6ruseWXOXDAAoNSWRPQ9qzDOcHOb5TQ7z/CaPeYaTY08+v1ptGv7Wdd2TXddNlh2bDfxl6rpkMBgMBoNhX6TW1NifAdoA5bruOuBVYBqweqo7ZjAYDAaDYd+iFqFBA0cV/11W/DsE2ArcOvVdMxgMBoPBsC9Ri9AgAFKp1E7goeJfzbiueybwSeBYYD7w+VQq9aVxznGAG4B3A03Ac8DHUqnUc8PqXUWoDVkIrAOuT6VSP51IPw0Gg8FgMAylFpuGnwGFKbhmHbAS+BRQbarOrwHvB64BTgTWAg+5rjuzVMF13TcD/wH8gFALchtwh+u6l0xBnw0Gg8FgOOipWtOQSqXeNRUXTKVSvwV+C+C67lfHq++6bgPwIeCjqVTq7uKx9wJbisf/pVj1U8D/pFKpbxQ/v+a67huATwP3TUXfDQaDwWA4mKnVEHJvcDwQBe4vHUilUoHrug8CpwO4rhsh1ED8YNi59wPfdV3XSqVSwR7q76hsX7+a9t//HKH8gWN6wbEcc+EVe7FXBoPBYDBUx/4gNMwq/jt8K2M7sLz4322E91KpThRoAXburg5WS+5X32RBb9vQg9ufQF9wOUKMGkvDYDAYDIZ9gv1BaNir5PP5KQuc0bPkNJzXH0UUY0wm09NBJejd1UUkcUBE6N4r5PP5vd2F/Rrz/CaHeX6TxzzDyTHVzy8eH30+2h+Ehm3Ff2cCG8uOzygr6wD8Yh2G1ckDuyZ68akMzbns8iuBKwc+9/zr/wfawVYe8XjLlF3nYGSsl9wwPub5TQ7z/CaPeYaTY089v5qzXLqu2+K67hdc152xOzpUgecIJ/6LyvoggfOBxwFSqVQBeLa8TpGLgaf3BXuGioiwW35/P1rr3fJnMBgMBsNUMRFNw5XAdYSJq75W68mu69YBS4ofI8BM13WPBfpTqdRq13XfAnwFOC+VSm1JpVK9ruv+APiy67rbCOMv/BMQB24pa/pG4C7XdZ8hNIC8FHgrcPkE7nHPUBQacu3tJJ3dkKxFa0RzM1Zd3dS3bTAYDIaDjokIDe8CXij+W7PQAJwAPFz2+f8V//4AnA00Ai7glNX5J8IYEbcxGNzpglQqVdqeIJVK/dp13auBzxb7tQ64KpVK7bPuloIADSitELtBtaQLBfC8KW/XYDAYDAcnohYVtuu6iwnzTSwDngfekEqlXtxNfdttuK6rAVKpVDUuC7tNx99zwz+C30z/eecy8/Ajprx9HQQIIbCmTZvytvclstms2Q+dBOb5TQ7z/CaPeYaTYzc8vylJjQ3wt8CjqVTqVcIATVMS8OmgRYReGcqfikCbldoX6FyOoLMz/OvoQGWzu+daBoPBYDjgmYjQUMrl8F/A30xtdw4yijYNeLtHaBBSQjSK9v3wL5dDG6HBYDAYDBOkaqHBdd2TgHnAXcVDK4B613XP2R0dOygoCg2B749TcRKXsCyEbSNsGyIRY+NgMBgMhglTiyHku4DfplKpPoBUKpV3XfdXhJknHx7zTMMoFING7UahYQhSovP5KduiELaNcJzxKxoMBoPhgKAqocF1XQt4B2GCqHL+C/i567ofTqVSJqRXrRQ1DSrYM0KDECL01ujsnHRbOgiQySRWW9v4lQ0Gg8FwQFCtpmE68F3gnmHHHwK+Thh5cePwkwxjo4VCEE7AewoRi01NQ0rtOQ2JwWAwGPYJqhIaivEQ/rXCcQV8aao7ddBQ9J7A89H+KPktLLFPJrMSxa0OPVxwsKx9sr8Gg8FgmDz7Q+6JA5aSpoGCT2FHhd0dDZHpUYjso5Ow1gTbt0NJSAgCRFMTVkPD3u2XwWAwGHYLRmjYq5S0CwEyZo0szSp0oEePsrGXEYnEkM/a84x3hsFgMBzAGKFhL6KL2xPNG7eS3/HTChXAk4IJa/ttm+jZZ2PPnTvxTtaClMbOwWAwGA5gjNCwF1HRDqx+sAIFmfSo9SYTx7rwl5VY02cjIjUnNK0dKdGZTLhlUTo0bRrCGqlFMRgMBsP+hxEa9iJ+w2u0NDzFy4nvcejiUyrW0WpiIoPauY3gj4+g0/1oXyMik+lpdQgh0PH4oJCTy0EQgBEaDAaD4YDACA17Ea0l0knjOxKRrJy+esL2DMonAHR3J/nH/4CISISUOMccg2xqmmir4yLkoEZDCwE1JEQzGAwGw76NERr2IqoUxTsoQHbL6BWlE3ooOK0gqttmEMl6sGzw8gSvrxw4HqRzRE6vLfK3lbQRsnbxRYMRGgwGg+EAYkJCg+u6rcCuVCplZoRJoEtCg/Ygt22UWgrsOKgCWPVgVRecSdg2znmXoTp3ogMN6V7UqpfR3T0EfdUbK2ofrKg1IbdPAWi973p/GAwGg6E2qhYaiqGkvwB8FKgHlgJrXdf9N2BdKpW6Zfd08cClJDQoLcCpvD2B0mBHwdega4scKVunI1unh9fq76Ww6mV0uhfdsWVYvWmIWOVc7EoFaDXBiV+IPRrt0mAwGAy7l1o0DZ8G/o5QaCgXEP4M/OOwY4YqGJiK9SjRIAFEUVjQCvxehvhSyETo5lgNibpwiyOXxX/0/qGXaGohcslfjXJ9UIXKcSTGRQgTt8FgMBgOIGrxw/s74EOpVOonQPny8SVCrYOhRko2DaV4DZURYewDBWQ3Qt/K8K/nBQh6q76WkBJ7+SnI2fOG/AHoni60qtwHYQuCPjUxLw7bRqfTo7ZtMBgMhv2LWjQN84FXKxz3gcq6bcOYDNo0jKVpEKFxgIwBZfYMXm9ocFAD1tKjsJYeNeRY/jc/DWNEZPqhbmT4Z2FLtB+E2yQ1GkMKywr1IkpVrxExGAwGwz5LLULDemAZsGHY8fOB16aqQwcTJaFBT8TDQAhQk4++KJL16Eyawor/HrPeiE0GIYiccgqR5cvHuYAIYzXYxlHHYDAY9ndqGcm/B3zTdd1c8fOhruteDHwZ+MSU9+wgQOmSpiGga+S0PIAA6rGxys0RhYTsBii0T6oPclqUoIPaw05qjb9mzfhCg9YEO3eCZWHNmDEkjoPBYDAY9i+qFhpSqdS3i66WvyLcjrgPyAFfTqVS/7mb+ndAU7494TC6oaFHQABDa8gkSB26Y04Ce2kD9lI3NLJMLB3TpVNlAyIzo+hsmsyPf4zu6xu3fREP+6ezWbNNYTAYDPs5NemMU6nUv7iu+1XgSEIjyldSqdToSRMMY1ISGmSnRd//tYwolzFF4sQehCPYI6aE49pICFAgksmBPBPZFSsGiy2LyEknYbW1VT7dbFMYDAbDfk1VI7jrug6hLcMFqVTqFeBPu7VXBwkloUEUNP6Wyit8Z3YOe2GGPAEBEgHEkWHmS6VB5QcrCwvERCdlAfnt4EUrF1txEM14PR5WnYVsbUXt3EmwYaiJixeNYp13XoXmBaq3N8xDYdtYDSONLg0Gg8Gwb1PVDJNKpTzXdWGoq6VhkqhiSOhgbo66xZ1DyvJrEngb46h+GxHGViRA4aGJIkL7BiFClT+E4ZqlDkNHTwQrGUadDCpoG4oxIkSyFZ1XBEIQv+IKgh07Bu+lo4PC00+je0dxA41EwkBPvg+ZDLq+HjHhnN8Gg8Fg2BvUMsPcRhjY6e93U18OOgYiQtb5ODPyQ8qCPhtvYxxvaxThDG5OBCj6sUa3gJDO0I9RTXyBN37KCiHHDlHt9yKEhqhEBwoRj2MvXDhQrBobKTz9NGrXLryXXoJIBHvJkoG02ELKAXuGAfsGk/3SYDAY9itqERpmA3/tuu65wHPAEFuGVCr1wans2MHAgCGkCsj0ZbHKFt7KEUAj/o4o/o6hWwZDxYvxqTu9h8j8oWdZiJoiexF4yFwHdqQO7UXRvkLYgy2I+vrQziGbJf+HPxTPCXCOOKJiczqdRjvOoLYhEjGeFQaDwbCPU4vQsBh4vvjfs4eVmcRVE6AURjpAsy2vqBNlOR4a+5CHOpAf+RWN/rDLSoREdkeRPTF6OgJU26CngypuccTH8NgYjvLzJP012IkGNC4qH2CVCw22TfT88wm2bkV1dKC2b0ft2lW5MdsO7RuKPRa+jzVzJsSqS8ZlMBgMhr1DLS6XteVTNoyLKk7aQmis+jpsqYcGXTyhABRqb1grsCNhyK0/xbDTUaQ3OCHr4v/bZV+/iAaIMWSIwKrHE4AOEI6uGMTSWboUZ+lSvNdfJz+G0CAcB5xwG0UQbleYbJgGg8Gw71OT1ZzruucAnyN0uYQw78SXU6nUI1Pcr4OCAe+J3eFQqYFk2K7elCTYlBxRpTzKgogF1F+yA2FX1mNYQuAFAUpplMqBJ5B+sa4c9hrVhRk7g40b8Xt7kXUjM3gKIQa3Jkw2TIPBYNgvqCU19t8AdwK/Af6tePhs4CHXdf82lUr9z9R378BmaO4JPcV7PBqm+9CchawzeAwAgS5f1uctyFkEvTZ2yyiRKYVAKUVXXxodPI/oklhJGwH48UPQVnmQKYv64n/tWreBYOasoT3TmngsRnNT/UDbmKRWBoPBsM9Ti6bh/wP+v1Qq9ZWyY990XfezwOcBIzTUyPCEVVMuNESBS/sHDwUBdm8/2nZAgHBCycF/og22JtAZC0YTGoBoJAJE0IFGWAI7YYPfjxWRaDsypG6wYAHWhg1EtEJFh5YprfDKXTuN0GAwGAz7BbUIDUuAX1Q4/gvgC1PTnYMLXVTPL+u/jZhfOWGU1uAHgkJkPttnfJYxDQ/GQ4hByaRcQkmEE3j2z03kVg6dvGXCJ3HKriGXFWXtiFInh+MUBYXCSJsMSbjVobUO2yoltTIYDAbDPk0tQsNO4Bhg9bDjy4plhhrpUPMASKgOyHSMWs8CooV1bG18B9noUuKiiizVgc+IWFxag/YReS9UbuQBDSIp0TSgcxY6N1QoUT0O/R02ckaeRClllgBdUPg9PsL3CbI5VFkODKfBhsjoQkMovGi0CjUWJVfNoL0dYjETLdJgMBj2UWoRGu4EbnFddxrwWPHYmcD1wA+numMHA08Hb+HFbYtYcuQCltVnR2xPzGwISEQ02Vd/SDzzAo63g96IS1TocWIsjCJRCIFfX2YQqcM/S/ehL1gLapjA8Oq04raFjUeWAImNCLc2IrKobRiaolsXFNpX6KLQILzK2x0CCJRCWhJhWehIBK0U9PWBERoMBoNhn6RWmwYL+CbgEI77eeBbmO2JiSEEHf4MmuUSeq3siOLGiEeiTiHrF0DmBeZkVzCdV5ECVGQGO+OX46sK4sNY4ZkrRWG0JdJRUPKc0AAK3eCht4JeU4dY1F801Sx5PJT+UyKkPxBxUkuB8sEqulTKdWsJjhuZPlsT2jYMdNmywLLQvj+4bWEwGAyGfYpa4jT4wD+5rvsFQvsGgNWpVGrkbGeoknBi1LryBJnzwuPR+jmwA6J9zxPte36g3D9sCZv9IyueWwsRYREpFNCeDCdrCWiFTKQJaISuKCLr49kAEmdAcJBoSyBUGul1AaBkA9oX6EQirJLJhFsUkciwqwpyuQKBP9SGQudysGNnGHJaCEQ8Pq4AkctlCaqwo4xGHBzHZNk0GAyGiVLzCFoUEl7aDX056ChNhqM5W+5KW2gEVvwiEnMkIggjdyd6HsXOrsUubCWnj5p0P/KxKI3KDvUIAmRUgFboxfnBGKDUk6vzKQCNOKHhYiaHsCIIlUGobaDziNjhKGWh58wdvM/+fnTL0NTfjiPJZHMjO+P7kN4enqcUwbTp4+aoKOQLRPKje30AeL5Pa1MjTc7ImBEGg8FgqI5a4jT8Anh+mMslruv+M3BcKpV6x1R37kCnJDT4UtJR71Ss0w5E/Qj10bcMHFtQ6Kcpu5Zo/8s02TMnfP1sZAlKxvEsm8CxkOjQVjJStrKfmYftUWhPYNfn8eMKjYMILMjmitqGcCIWgUIQoD0FwkbNnoPcugWxbSs6Hof4oLGklBbRaAVhoNw9M5+DyGD0yNGQQDQ2SkrvIpYnUcat02AwGCZFLZqGM4EbKhy/D/hYtY24rvtG4MvA4cA24FupVOrfx6i/EFg3SvH3UqnU/yvWux34uwp1nOLWyj5LJm7z2JH1Y9Y545VO2vrC1XS+KCg47feyhHsnfN3+2FGsmX0zYdrtMBeG1hoZMGi6kPQRRNHP1KFfSOD/dQc5W2Frja2H6kgEAq0yaC3wCgqRTCIB+8/Po19+idzb3gb22AIAhEKAKBlNeIWRNhpC1JwhUwiBUiZFisFgMEyGWoSGRqC/wvEM0FxNA67rnkAYUfLrwN8AbwB+4LpuJpVK/WCU0zYBs4YdO50wPsTw4AaPAW8vP7AvCwyyODM7BUVLb+UcE5moRS5q0Z10BoSGnuQZzGh9BlXoJ1uYmMFgMv8KifzrxRgLkkxxQtVKIMp2DawFGaJ9Nnang8hLsh02vQ0BSQKSeQ/lDIaDFlIiC1vQvkZvFaQXzKOhv4fIzk6k59HXtwu/eexXRaFJYpPEDu0aiomtBtAKYnFobqncwCgIhNE0GAwGwySpRWhYA1zAyDgNFzC6JmA4nwCeTaVSnyl+ftV13SOBfwYqCg2pVCoAtpcfc133LcDKVCr12LDqhVQqtZ39BFmcbBt7chy7sq9inddnJXhlQQOZMlV+wWqBo65DBbB669hq+dE4cv1bsFUfdtANdtlEPnwBP83DO7sT+UgrVnuUWM5CNisEEksIrHLnTxEDO4awQeZ68Orq6T/vXBr+7xEiW7cR689SaG4ds18BGr+kv3AcQked8grBhKJHCikIjNBgMBgMk6IWoeF7wFdd140BDxaPXQj8C2ESq2o4DfiPYcfuB651XXduKpXaPF4Druu2AW8DPlWh+CTXdbcDWUITvi+kUqlXquzbnmfAEHJ0kvkwQNPaGQk2TouTzAWcsbITAEuWzq5d21CwZ2IX+jhs03tASLKRQ1gz62sgKrwSAnQi7EfskUY4qY9gcR4tJELrii6eWgjwQiVPUBfGhqh//Em0lBQWzKf/5JMq9kuGVhGjd1wIULVHjyzFhTAYDAbDxKnF5fK7rutOJ7Rr+HrxcB64KZVKfbvKZmYxTGtQ9nkWMK7QAFwFKOCOYcd/R7j1sRqYAVwLPOu67kmpVOrlKvs3gnw+v9vU2qWpVmuNn68wESpo2JXH8RWeLfGkoLtOsivh4Ad5bAsEGn8C3euJn0SisApLZ0FDXe4lIvkNZCOHVKzvzUhjrw/dKO11DukFGVAgu3MgBRpNXITxFbRtI3wf38oikjGy06YRW7UGEQSIICC6Zi1dxx47aljLAoqCHkUY0ho8D53LD9avFHVyxGmaTC5HT0+USGR8u4qDiXw+P34lw6iY5zd5zDOcHFP9/OLx+KhlNblcplKp61zX/SqDqbFXplKp9CT6VhOu6wrgg8DPU6lU97C+/VfZx5dc130UeAX4aPGcCRGNTkz9Xw2iFBHJEjgtw+MYgM4H1GXhjc+140vBnw9pZGtrjFzMohAIbEsTizAhu4b21vfS0fwO0IqF7f9KffZ5YsEOCnJxxfp6gUe2oZ34g9ORGZuYLaGpAVXMO+FriOiAWDqNtm3QNqgAGXFQixbSOW8uQima774XmcsRDTxUbGS6bgCNwiYyYPMx8sERelmUaTjG854AUEJg2c6YP4iDFfNMJod5fpPHPMPJsaee30TiNGSAZyd4vW3AcB/BGWVl43EucCjwnvEqplKpguu6fwIW1tLBPUlpzpM6TiTTAmi8aAfaDi0RhSVBK4SGSKCpy4Xq/tWzkqQDB8cGvwFKTgE5TxCMEiiqHKlhybY0jZlQc5C3Z1PP80zv/hlN6Ucq9ZSuunPpa3hDeH7aRqYFui4sAxAafKUHIkUKy0YEeQZ2L+wwDkRQl0TmctQ9/Qz5RQvJH7KoYh8VjBEqW8Mo2yJjYUmJbxJjGQwGw4SpJU7DBUAmlUo9Ufz8AcIV/CvAR1KpVGVLvqE8AVwE/GvZsYuBDdXYMwDXAC+mUqmnq+ivRZhM66kq2t0rDHgdaAe70BQe1JJC3cZihaK9Q1H73tDrwxzoSTr0DDcQrBFfCt6wqhuAXPQQ6INk/jWS+dcq1k/kXuO1+W8Y+GyvieIdNahkEkBBazw/wFY+BD4y6ERvVYg6G6SFsCRBfQSnAyLbtuPsaCe/cEHoJTGMNP4Im0yAGDa21qFXRfE8kS9AYXz1nBX49Ody6PKsnELQUJfAqtAHg8FgMAylFk3DjYRGj7iuuxT4LqFR4xnA14APVdHGN4AnXde9AfgJocvlR4CPlyq4rnsSob3Ce1Kp1DNlx2cAbwb+cXijruvWEQoi/wtsAaYD/wQcAryrhnvco1hOOFEVZDeFmEMkNxOpyrYpLIFskAOWknNzBZw13XhWGMUgZiskYElNU0zhKdjWP9zbQKO9wYza6ZjFq/Pqh3hj7Kq/BM9qQ6qREcEFAfN33kjE3w46wDu0H2dVHRTCqJElJGFOzd5kggYRYHk+Mt0LfV0IaYEVQTgSz63Hn9FG4vkUslBAZnOoZGLINW0kQTF2RDkeodeGbTtQvodX8KoyjrSVQqsc6VhssE3PJxGLYkWM0GAwGAzjUYvQsBgoGRS+BXgolUp92HXdUwhjJoxLKpV61nXdNxMGd7qW0Ajyc8NiNCQAt/hvOe8lXHPfWaHpADgCeCfQQpiq+zng1FQq9XyF+vsEJU1DR28/L0cKNGY9NAq/a4zQElvDiV0TuohAuMqPxHy0hqhXIAB8JQitA7LovBr4pi1LEvTl6A40z2fySKVpzHgIjh71ktNFIzHdQ2P3veSazgFchCeH2CkKwkt4toWWFlpItJdEIxFSgh1BS1COhZ4TI1hVj+zsJLpmLX5bK97MGQOaAwkV7RkEkgA1MkCUpkJuiwpojVMogGMPbG0opYZqHgwGg8EwKrXaNJRG17OAB4r/vQUY2/m+jFQqdS+MHsYwlUo9QgWz+VQq9W/Av41yTpZwm2O/wipGNezeupXntwKs3zMXfnk7HpCqsvqx01tYEO1hYde3yIr72cyvwRvNnkDgFe0NLKXQCEL3Dh8CC2UJJP2oeGi4mHwxTGPSe+pJFA6pbIQ52LLAR+Mx1F3EQ2GH4snYNyJEaAuRzw8IKLpQIMjlRub/sO0w86bBYDAYBqhFaHgR+LDruisIDRKvLR6fR7iyN9TIRScfxf+m+8gGNloIZupebDWxLIxS6DDZVHHy85UF2kYLj0AMdXDJRCU5x8K3BAVbksz5NPf5o4Z7+GPwN+xKP8RxyWeI6rWAQhQqq/MFmqwWKCFxLAsUqKxGUwBs0GDJHLlpUSg0YvdlsbIF7B1bxhUaJOCj6WFocqq89NBY1FVj5yEldHcPfLQKBVTgEZR7X/g+orkZq6Fh/PYMBoPhIKKWGeqfCeMgfAL4j1QqtbJ4/HIm7k1xULN0djMXLW/hsfxhZEWS5fZzJMtjOE+A+ck8TZGAjV3NFLqWoqwMhfq1Q+poNFrA9roITy5spDFd4LSXdqGDQduHcvqzLfzPMwtYGn+VpOyD2BoCv4kg3Y92HLQcOllrDXkBjpAI/DBPxIC3gyCw6onUNZFum0FszWYSazcjqjBkBIhU8KnQWo6wfxi9gaHbGEJIVCSCKLNz0J4XZts0GAwGwxBqCe70eDG4U/2wGAk/BPZYrIYDEbu4cva0BMaI1CTGN9YrqFBdMLu+n/VdIIMEse6R6bOd+k30eqHDS0cyQn/SJtnloytcIh4NX5NdfgvJSB+HHv2msGAH5FWU7+z4DDv94elBxubYuQ28YWEz2g6lFOH74WRtjaItGGvnQYOnNTrcDKnNFVOMtGvQQqALhYFw1aLK9mSNLqAGg8Gwv1FrcKcA6B52bM1UduigQzjY2itOXgGWGOnBACBUDt9urBzmuYzegkVzJEBIj3i8i1xupIpdawtRaKDO7h441tkQoa7bZ/iSXSuQjmDRzHpeyJxCm70TURRsHOERlXkWxtbTlZ09sjOj2Bf6SrO+M8vhM5uot2ySQOApchtfIR9ZQFBMtV3ejqyTyEqptAHPEwQ6oAeNJQTzI07VE7iUkr7+DOl02XPXGpHLore3g7RQra3jCiJSSmZNa8WyjBeGwWA4cJnYBrphylCRVigkQEN/bDFJZ07FetHMaoT2GS92UyaweLWnGBksthliQ8ujysHqOQwVRJGA254mNT1JJm4hmivtTQToAE52pwNX8zpXDxTNf/Y/aW79GSfPS7Jk+sjw07LgYWdyKKf4mmnI+4qfv7CddMEPX76ipkFqiROxwfHxnaE3qX2NUBXDORQvBHEZXiOjFD6aSJX5OGzLwq5k8FiyccjlIOKMm4o7mysQBIERGgwGwwGNERr2AWyhQMOafD2bCpVDK0uVBB2QVAWOslaPlrZhXDwRBk3y/SiN6YUcu9FC+z6bpgf0NQy+Dr4S+FqE4SYVxL2A5Wt6sMq0B4EM+xrdoIi+FDrQ6IjGO6EbHVfF1flQdUPEEthS4AWagq+I2uE1o919NP9lLek5OXoWDE+fLQbDXlZBoPVEcnhVRhBuU1ThSaGM66bBYDjAMULDXkcQF2HIx6yyqbw5ARAa8HVpmKe30yz6J3Q1JTS2k8X34niFehzg2PWK12dnSNtjT4zzd2aZ0TOYHMqPhaE0LC+N1THofaA2xfGXptECRKDBLhpBhv8jGbHoyfn05fPELAstQAYB0Z40Vm49mZmtYVZK2UDgNIIFqqChP0AmrXEFgg5PhYJYkYRTc8TpAQQ+umcn2rJRdcliuu6RRNB09fRhj/MMdwsaGhvqiDjm52wwGHYvZpTZ2wiLGU6eGYlugkoL1SC04reDXawptNJJK1liNDMxoQGgP7mGmC5O8j2HEAksLlrXhZZhhMlZCQ+lYF06hvYUq9oSbG+NDYkiCeDNtaET9NwOcod2YG2M4ayuQ6TDetqyUPZwdb2grig0pAs+rU1xti8/CjtfoO3l17HyHtLPI0VoHBrQGE74lkBlFTIxttAQExKFxi8+S0+DAxVDUleFY4NSyEwGHXUGt1rKKKCIRyPoQOPtBa+LfN4jHosaocFgMOx2zCizlxFCIoSi3h7Fa8IKQPnYOk8jPXTSyhY1jayemuybdsRhpg9NO9vQ0idwephr9WMJiBe9CnqyNtuJ8cLCBlr6PRoz4cQYWOH2RFyspNW5A79tIfnVbx0QGhACLcXQ5FICkkXhY21nlt7c4CR7tmUTC3yyL20ksAR9kQg7mgYlBK00skuOUBuEtgSVxYJAQ8SCiC1ZMn86sWgVkSPLkVaYN0MBvkJUkFgkAiEEzt7QMgBKhTk/DAaDYXdTS8KqBPBp4HzCzJRDlpCpVGqkJZyhKgKtRkQ5HEQBAUII6nUXCNipW9ipW6bm4hFYZyvemJkW9sWro9DUS9zWzIyHq/1Do3leBLQUPHNoExe80AGAZ4d9iHvrie+6DYD1seNQ6TJjTiFH2CM0xMLXblN3jk3dg3EpDteSBcDirs6BO/9WZ0C2ClfTaujtz3PCEcMCSFmMqbmQAmwZ3rvI5xG5kdsTEoWOWGNYau5epBTk8nky2YmtAXK5fMlZdVi7snYhy2AwHNDUMsp8H7gM+B9gK6M61BlqIYoggU1yNAW6FOSwCGxBWzzNoX47HhIryKB1AHJy2S7XqdlkpCQX2UWs0IJQUTZnIjRGwpVrS8Tn8HyGozb38vLcBtIxayBtdSZ6BFta/x+O30Fj5nGi3hYisXUUMgsGLyAEAjXkZVnclsAPNF65MKFhbcGhkA5jR8xN9xIPAo5v0nQkWtFEQuEjIsKZvEyQ0EqF+S1Goac/w/aOXfT25+jLlAlnWiMiEsse2+ChIQrCshCFAlZ3z4hy2y8QTHMgOrnvYqLYlkUu79HR1Tuh8/P5HNFoYejBonZo3qxpVcepMBgMBz61CA2XAe9IpVIP7a7OHIxYSGJAdNRddwslJTkpkXaS+cWjdq4HJ7cZZU0u1PE21UaWGNtjGZr9NjRRCrlWdubDCX1GzCdmKw7plmyNRPFsyY54K41BjkB69Cb+GoDFW3PM7tpCtP4FvPw8ujJxCq0RpIwgvQA9zBVxfkPl7RVfQ13Gg1fXQGc3x8Q06ZY4gawPC5MCv6ke7PjAOUHgY1mjv8pb2jvZ3rELXwVEytw5w3lRI8dwRfFCxxaEEOho5T4LNDrYe9sDQgjisYlrBCSaaIXzc7kCSmksywgNBoMhpBahoQBs3F0dMYxOpTiR2ooidQGtCpVOqQotLBIiR1bH+JM6oiyv6JGDeqSSO4dFmJoM+AtwUj7DDAr86sSd+LYm5xzC7C5onflDWmf+kNkbZnP79PvYclLt+/yHr+vitLXhJNa4YSeNG4amNumbM4fO49+AlqX4D5rBJyQYvt9QsjXwvApGipoxdWZagxdAuTJCiKFmFVoI/Hwe3xmaE0OO6El4YLes3IWc8u0RjSbveUT0JE2fBJVjYRgMhv2OWkaD7wLXAJ/cTX0xjIKoYH6nZYzAapi4LyEa2+9igbWNQmCjEQhtI/TIwV0UZ1WFoCAkeSHZaVnMz0tmdUF30mPDtHPY0vIwdb19NBQ24ES3csjrO+hbNC0MKxku18fsUWAJ0nGHzsYYmemtRHv7ESooCgUglMbOe8S6dhLNvEYutjSchJWC0kpf2iOiZpa8CkZ4NgjQgSZQo2sJhIJ0WVoMTShA1JXtRGgl8Pr76O7uGzimgBg28WG5MgRgx6Z4AtU6DEbVUnWy2aoQQtDR2T0VLTFreguO8e4wGPZ7xvwVu65767BDf+267rnAC4SahwFSqdQHp7hvBw3jGYeMDJEEykqQSyytKh/FaCT6X2Cm6GSms2vM3h3VlEUKeGlThK208efokWSKcRDesCpDIHuABB1NX6ejCQ5d+1ESrGTxto20vRT2z05nQleGMSIm9iYcfnfKXNIxG68uQfuxhw8pt3J5Zv3pZUQAQqvQVkLYgA6fg6YYiGlou04xgFRhmKZBIGAc1bslhzanCU0ryhN7WZbEGha/IUChkIhhPzEVaIiO9ACZFOVC0xQyVUaQ2VyeQKlqcpAaDIZ9nPFE/0OHff5L8d8Fw44bo8gJUq2qemStyU86SkQIk02M9RoICkoQszTu7ALNhV7+3Ac7rRi/TUSBMBmWozUn53pJaoUvF4NaScTbASwEQEuJ9P0xX5Rk0f0yE7NDxcTw/pZCTvsBaBGG1R7R95FXKGkasvkCfekM9cnEiDrVIhgzpdgAEoGqVHN3mAeI2iJm7g38vWjzYTAYpo4xhYZUKnVO6b9d1z0TeCqVSg3ZuHVd1wZO3T3dOwioYhIpbU54KBQaB4ksnVhpdq0SZSVwCjvRwkJqH89uqpgbu9+ziFk+EQtmxDLUpXP0qxiZYQLPDjvOEs/DjyyAHET09oEyLSVSBWhPIbQmiIxcxVpKEy0E5CMWuahFPD90otHFfXERBKAUkfxGNBZa+ajodHynOVTVB/khD8UuEyS2bG/nsIUVkmvVgFCghRpXWeDZNr1FhZwGYlg4WlLIDZ7rTIXWQRRjYZTHw9iHkFLS1dNHX38GCMNt1yfjNNRVDpluMBj2XWrZZHwYmAW0DzveWCwzlk67CRtBU1G5myYgQBFOipMzfCtE5+FFwgk0mltfTIg18mvcmnXYmQtflfnJPNe0PsGrO5Ok8zYCi43WdNbac+ixusjojSTjjZADR24NFRkWqIhDvrjij/T2jzrBJbMe+YhFOmaPEBoQAmVJZKBQKo4ubc3oAjLoB6e54jMRQrBsyXxeWL2RdC4/orxWqlnTCzQRPWgxqdAU0ETKuqeVnpTQN7Jj+6bQEI04BGpQ66L9gELBG+MMg8Gwr1KL0FBpax1CoSEzNd0xjEZJs2Ah8AcPFtXSE02sIAcmXiXjWH4XmkpuhQKvmF4zryRJEbCooZ+8Fx4TvsNabw7r7NksSnQSDVqgCxINT7FwjYNuESilUSKHkg5d1iUoXTnddCLns6sRXl3YNLBdUcIKNDOetZFBgaZVmwa2K7QKkKKdwO4c9VaPzeSY4fWwdrvmT2WvcTwa4fBFc6pOpQ1F90shxp6fdSlcUul7A4VClBlp6tF+URNlH06YZZV5dmhpofbx7RSDwVCZcYUG13X/s/ifGviW67rlOZUs4Hjgud3Qt4MCCQRKhwZyAIIx4wbYQBqNHtgv9xnMlz006NHY1xVDVENKxokEmxC68t6zlhIl68gHEgioj2nqY2GfA68PusJ6fygcybLWV2EjONGtNPLfMCzmkEhAZ+TKsM1hFvUN6XAFur2tst3BKY0JWtoLJLZVEhBGFxoagNnAnP4CP9o09Pm2NdUzo6Vx1HOHo4G8qhRQuhwBKqBcKvDQaAYjYGpFaNRpjzQRtBDYNQiDMu+jOnrQY7hdRi0xRDiyLIGQApEvgL8HV/4qQCMJohPzphCOg4zFxq9oMBimnGp+tfOK/wrCcbfca6IAPALcNLXdOniwbIETlVjR0PrfL4xtZmcjaSzZoUsLRNE2QGtQHtVsWQRoMughQkNgN5BNHlH5BK2I5daiJHTkbbyCRnqUvT05zrZe5JHgGAIka/15NE/7JvJPYXyFyJHg2fU43rM0pe/CYQdefTL0qBimUl+6sYdEzicY5tXQk4ywdm4DT1ywjNOfWIuIiIFb1VojCRjLRFEoTcOqHbQJxfHuQhCCtVvb6epNk69RVW6hKYxn16dL0TUG+xQwzOWodNAfeq8acJDEa1AEisCGntyo5YEGyw5DYgOgNFbcCr1H9rDQILVCKx2G3q4RHQTIaBSM0GAw7BXGHZVSqdQFAK7r/gj4WCqVmlisWkNFhBRYUmJZItQuj7O4lAgi5ZVKNghCg/LHbwAIEGQZWldLh0COvtrWeRu0QgtJd8FB9akBeQUgTjfNTg9dspGXc3NYXO+TsJuxtsaon5+hLzKLqDqEJu6iKfMoO/xrCPLTwx5IiW7OgaNwAs2ibSMzeHbVh0LDttlNrDpyATIqBt/eQDG9v4A1lsZba+rW7sQOFMschbYsYjJge5CjoXMncTG4FaIti1xL66jBkqSs1ppkaK2KPzatim6fg3VVMeJUTQ6P9jg/ZQ1KgixGqVKBRjsCYRddVSsYpu4uhFYopRGjRNgcE6VgL2QSNRgMIVUvZVKp1Ht3Z0cOZvK+j1UQSMD3guELz+rxPcItCigJBKJCBEINePiosl338SZBT1nYug8t42hR2eM+rnN00cgaez4aOCq5Cwvo+1MC6MF3psGxYd1Dc1ex/oXfE/htAKhpafw3bK/YLkAiG04U6YTDEyfMHFF+6NZejtvQNfoNCEEQjyD7c8z40x8BGGhlTResGVp91+FH0nOoO3p7U4nWoUtGMZqlIFRATCWCYprwot2DUkCgkTJMIy6LNgbW7opYOaQvAqWqcVytcK6UaN9H5/O1G33a9pg5SgwGw/iMF9zpAeCvU6lUT/G/RyWVSl04pT07SIhHbNoaLLJBGPPHktbEjeMCCJeUPmFGAYFWlRfM9VioomVEgMYZR0PhR2fhZTtJ6h58WTny4MJgM9tlG0pY9Mo6/CWbcfKShAWeF8PzZtHZ/VFam76FlB727JV4mROQHQlEz9irzqivOGr1Ljqaimrp4jMqOJJdTTG64+OHDupdPIfk5u0Dd9qbVfTmFPUxSWM8fEgy7+P054n0jExMtXsQxW0lDVaE0JohtJCcSscKS0BBg1fchdBBKCjK4jGHoluohLo9oXSYjCGklAQ7d9YmNPg+oqUFq75+4tc1GAzjahq2MLgpu2U39+WgpCkZZVY8zo6MwhLUZMU/KkEGVIBSkkJOUynsf1Px3wKaHoJxVeG+H6drVz0J79VRrfQbdZpTvT/zeOQEssTQDT6ZU7qZ0ebREFes3d5MV+YLOJ0bacj8Go5aQ65uCfFfxBEFGwLBWHsMh2/ogQ1DJ/P+uM19p84jHRtfaZab0UJuxmBK8de29vPM6m6Wzkhy8qHNAER29TDtT69jZ9PjtjcliGKuDK2KGofBIsXU+TELIFqeL8MKL23L0HY2IkO7hz3i1CDCwORa6wlpNcQE7Bm05w1KTAaDYcIIvQ+7ae0uXNfVAKlUqpoRa7c+oGw2Szwepz0bbhZYUyI05CAooLHIZysLDSV8NLvwiY6zplVK09Gxi8ZgLQSCoFcOsWkYbM/iwehp4YfSu1XetIYzd36BUzu/Wvwo0Aj+2PIJ/jD9hhpusqzJsvataJaLe54cEtBpNNbuSPN4auiWRoMO+H/59lJXWSuj/DzSUuHsoTQm41xw8tETT8ykVdGwNdSYeCjqsGvyoKjpckU1hu1ICl6BaCRCyYGnMbp7tycA8vkCM6e17LHtAh0E4e9r+vQpb7v0GzZMHPMMJ8dueH6jDgJV2zS4rptIpVImHsNuwhIQlLa1J4u0w6iI2gvDHo+ywJKOXXS9FKUYTKM3KQW2ZZErRLALHWgvgEKYLEpF4ihZWv15tFoddNptldXHAtbUXcLxXd8lqvqLIoPm8L67+MOML0/odsuvovIJdgZ1zMz0IOrkmM9zWkOUmCPJeYP7671INguHucVnt1jliWpFfhxX1p50lp7+DK2NE1V/y6KLZlC8J02aCQSishzKLVQkgvpKP/Nisi5PKQJfoxx2iy3FaCit2b5zpA2KlIK2lkasqc6KKSU6ncbfvHncqqK+Hquxehdcg+FgohZH6S7XdZ8B/q/49+TwkNKGiSMFTJlNuLAh0hiqpCuYCqjAx+sLPRQkEEeQHuaCWYmW1iYGNzaKl9q0EZudKHtwsjyXjWg9NIu6LQMWNHXjBYL1NLFu9qtYRMlaL3LMhvNp9DZyWcdvR4SxFjsSOH+ahZqWwX/Dtor9UkojpOD+2acR5OroSSaZ0d+DGMcooD5u89cnzxpZoOewGZjx5Es4mQJvf8ORFMaYRB5/IcWW9i7S2fzEhYbQjWTg44QiGAxkEx1sxy/6Ygx/DAIQRbfW0laBFGKPxYeKxyrbsBTyhTBS5hTLDEIIqKsbt54OAshPPmqowXCgUsvYdBlwDnAR8Fmg4LrukxSFiFQq9cfd0L+DBsmeC+gnpBiivLcG0jBNQM1h21QKWjBcyRDocCKL2Jo5Tf105At4XpwGawm+NR07aOeQni+xtflr5ElT2hUSCT9UK+9MwKstBIftGtHNkvdHhBxZ6lgVWcK6BfMBkJEc8aZtE4qufNbrG5i1sYPY63/Ba0ry5zOOJpccqQJMb4tBO7yyZjPrt3bUfqFJ4jgWxy5dQDxSMmccSlUGlToUvIoSxF4MRy3Yq1umUkJhRDQNg8FQpBaXyweBBwFc160HzgLeCnwR+BIm98SkkEXr9iAYZ8DUELUmZzAphIUQEJT5uysrGBl4qDzkMaGF/XAlvY5E0GnNkMBKlqiQclpQCCyidkAy4pFNZOjuacT36sg7x2AHD5Eo/Imm3CO0x89D69DgUSc8tKUQgcRa00wwrw/qKiu46oM+srShC3E8ipN7rpHe2Vl0vPaJYNP8VmZt7KBt+y7atu9i09wmnjttpBtmsKAOXi5uUaSzFVra/TTXJzlswexwi6M8vwWaDP4YLrUWPgrfFwgFBQU5f5iQISEelxWziNtI4lP409dFr5G9hRAC7fsEuwbTxYtYDJmYeGZUg+FAoiYtqOu6ceAM4FzgPGAZ8DKhtsEwCZKOIFrF2NtT0CgdChmTwamvC+d5DeR8Wuxh1op+jnI5MENA6Mg5FFHnILFRxeBCOq8gqyoIDbCtr4GoHQoq2WAbRPuQUsJh1+Kv6cHuexY72IQQ0UGti63xztxE5OEwG7vw5KiTyvHtq1jX1IsnLbSG7cwnQwOHv9ZPE8VJQIWykIiOb4AnZrSSOuMwmrfsYvradtx17cSbk0hdwHNmoqwY69sE24+YxuE5h7bOPZ/+eWtHN2s27yCdzRWzw4hiEIYQqzgJjxIcHCT4MrShKL0P+WEhFIICyKgiMuw71UAef0qFhrDdvWycHY2iS9qGIAi9LozQYDAAtRlCPgqcSBgG5xHgK8DDqdQw83PDhJBCUE1U3ZilSU+BJYmU9oAEILzwRRh0fysptAcnCQdBocJgLiyBsPSgUkIJ9CiL7UBLMl7ocpH2AgqFPhzbYn59gD3/InjlWeL5Z2iLLETKPJnkGSirGZLgzfTR223iVgueHYRGo6U+iPAvArg9g6GbVTzP+igUCrMJ/IbBWwPIlWIhpMNomqOQSESxGnygnRlrdqDmzUbYWQqRAoEdY1cd7GgUxGc3Mmcv6No0sGbzDnZ297F6044JtaEQyAqGnm3NDTTV1+FrBUpW9MIpFHUD42XiqB6xm/2VquhBWXRNbdthICmDwQDUpmk4iTD10FPA48DjRmDY89hSUFBhmOGpIgCkD0LqsoRZAvRgqGlLSBQeAXLIulJjIZSH8EPDSi2SVd6HJOP7oDX5AtjxYopu72Wm7XoZACfzdtpbvxe2a/UAefzsTKSMjRbheQj1xegTmyMtbK7CbbISjva4LD6YDb5h9WZ6D5uODPoAScSLADF84WH5eZR00HIC4ZEnSF08vFZ3X4Y/vbp2StuORRzedM4pCAFeAE4FocEH+lEDGihnmC5KAE5NarG9rmcYghACrRQqkxl0D41ETGRJw0FLLUJDI3Aa4dbER4Efu677OoOGkL/ZDf0zDMOR0DyFc5IGun2BY4cfAk+Fex9WNAwbCKAK2BqSQhYTXZXnrIjiJRaC1ljeToTOo6t4rWzbpi6ZBK3Z3hfQFDuE2Nxr0H2dSL+XRPoBYsErRCM7QIAXC3NmxGQPTrKH3r6yexjFbm9+QZKjGU+EUTaHrogthIygVQZtjcx1AbDZmoknHPrrmsi2NRLv6MHuz6JEFMfvAb+XRK4ZmI0iTTS3mcCqIx9fWMWTnxoa6xIcu3QBfZkJ2lLo8iBLuui9Ili/dQe5gkfB83EsC6UhXcEsxEeQHvD70TTgYJUJDhpojVZvWFny5NinsCxUV3F95HnIGTMQJqaA4SClFkPIPIPulriuOw/4AvBh4B8whpB7BFsK6iNTa9me8cAqhjQIvOKALW0GXg/lYyFIYBcTXZUhbYJYyW0xQOY7qea1siyLeJm+u9sDmt8LzWB5nSx87QEi+VcIcqvQTn3RpbOOaL6Xtmm78PwIuhjVSakAWQy/HajyLRVYrraGPfN9+jNpHCcMniRFC47jEgS9BM66iu4FPbKBbtFAt2zFPuxwFj7+NFamgMxJFKH7XrJX0NgbEMgkfbFpWNojb0mkhkgFH1ot1NTFhiZcCR+2cPak2lB+gLStYvIsB7Do6O6lpz/Nrp4+GpJxLD12zAsIA4XlCbDLhIaCgjrLIepUN9QICF0u9yHKE2tpIcLokkZoMByk1GLTECM0gjyPUNtwHJAHHi7+GfZT7GIIYUuEq/YgGBrOOAy5qEALAjQ+YXKtki3BQDUZR+oCSjjgq/EzL45CYDejRASpCyzY8ha2zPoPlH0EGuj5SyNaC+Zd0DnsrHCG7uq26O4ZmYdieOIuTZhG2rKasNRxFftRF8TplvBs9CikE/AB/ohdKDDrsT8N1JkFHCskXSe+naAuNNasG2ML3Ldy5OK7Rq+wtykmz0rGo/T0p/nDcy9NuknHsvi7i06hLj5++GcFFAKf2BRtUgymAJsipATPQ08w4ZbBsL9Ty6jeQ7j9/UfgHuATwB9NgKf9n5gN/V4oOGCHBuNDxuyS0FBcQQaEdR0Aa1Bw0FYMrBjS8RGZPgLVApEJCA5C0tv61zR1/BSBJpZ7CW/mQtSr9aAEmfXxEdmRhQDLgnhM0V0p19TwTJ86i1LdCEZfMc73NF1W3YAz6auLlrNg2+vYWuOU4kj4eWTgo/s2k2luKl4rDI9tKUXUL/ktCARRrCBaZeCEvYEsJjuDRbNa6elP15yNMrw1MXB/hYKPFwS8uGMXs2e1jXu+7wfYPd3MiCoikfGTkI3XlwQWjbUlGR+bYmTJIFvcDrIsdEPD1LVvMOzj1DKiX0IYBTK3uzpj2DvUOZK60vicqGDg5RdCNz4hcAAv0PTlLaSnhgSk0nYd+YZjoAHszlegL2CCsQ3pnPURlIzS0v6fSKcHNTeB+Ns+9E8a8PtsNm6ODcgBSgVEIoJ5cwrYduUVqhDDgwZpPP/VMfvQ6sO5ZW/75kWz+MnRFzHb384JfrgCr1u7lcbVW4hkV2PrUMWwKxHhoWNm0ZgucNGL20J7D5VG6rPDoN1ahtsU+xplUSnnzWxj3szxJ/nh6GJwKCsRahWeffF1Vq3fip/Lk6jCeDBwJIGniShBdJI7ngqNz9Q+Z2FZkBw09tXZ7BAXV4PhQKcWm4YpicXguu4bgS8DhwPbgG+lUql/H+ecRwiDSZWzJZVKzR1W7yrgM8BCYB1wfSqV+ulU9PvgRgxkYLQAXwwmPBo1kKQVQ+heNBO32vSd0FaisfMukr2PhgePkqAF2Z3H0TX9A4N1/TAEsmXB7FmV9wdmBc6EsiqWiPs5/pyBIBrnkOJ8GmSi+KuhfutOGorptGcjmPlk6AFilfbntaYu9z9IRDEQd+gBo+TUTDh9CxbRt+iQKWlrMggBKtAD1ql1ReHhlVUbWbtpe7GO4OilC5lbQfMgAQ+BmgK7BgEV3YSnnGDPx+cwGPYWE1sGThDXdU8AfgN8Hfgb4A3AD1zXzaRSqR+Mc/rPgE+WfR7yS3Vd983AfwDXAvcRhr2+w3XdXalU6r6puYODFUkp9oGFwBMBnoACYTIsUXStt+VgTKdAxrDULtQEV3oOgnziiFDNH/RiBb1hQXE3IZpJ0a3ehZal7QVBviCIRTXRyGgTxeTc5KYFOchAn4oRK2q8g1lJ+gVQCNCFbLEnMG1SV6oda9VKCjOru7/ArsN3atciVIsAVK4A0qalPszFkS945AuDO5mrNmytKDQIQGtBMAWrd4FAoekkV9GuoYkIcgr2iXR3N8GBmnZba3AcrObmvd0Twz7CHhUaCO0gnk2lUp8pfn7Vdd0jgX8GxhMasqlUavsY5Z8C/ieVSn2j+Pk113XfAHyaUIgwTBQrAkE4ITZh0yBsZsfCTIUUA+Vlfej3B6Na6iCBr0BO4BVLF+XBQmwxGw67G8vvHihTT8SY3fSPONEt2H47XmTBQNn27REcZ/SVpVcokPMK2BM00NRkEQlFhiivrQUbBUQQpx6DLJsQW5oF0TrJ2h5JJgPZmMULS1tJZgqc9tIuQGBb8xGynkCsQYveCfUHQGjNtGdex8oXwhXvuDERAhyva7cKDVgSrRVoRVtTI5efeRJ5L7SV6M7088wLr49uK1EMETJuOPUqiWFRKapJgQAfRWSyTl+xGGQy+56b6FTS349uapqUls5w4LCnhYbTCLUB5dwPXOu67txUKjVW3tq3uK77JqALeBL4QiqV2gjgum6EMFrlcMHjfuC7rutaqVRqQjrEFStWjDi2ePFijjjiCHzf5777RsojruuydOlScrkcDz744IjyI444gsWLF9Pf38/9999PdFgqymOOOYYFCxbQ3d3NY489NuL85cuXM2fOHDo7O3nyySdHlJ900knMmDGDHTt28Mwzz4woP/XUU2ltbWXLli08//zzI8rPOOMMmpqa2LBhAy+++GJ40EsDodvdOWefSV1dHWvWrGPlq68BoWtdXwEiluDsc88l6tis3rSZNdtfGjHYnHv6Kdi2TWrNWjZs2jLi+qedfRoKzeuvr2LL1qFyonw1wTuPm48T3cL657/Ppu44GkkufjzKaiISiXDGSScA8MLK1+goyyEQBAop4dQTlgPw51depac84ANQl0xwwjFHAfCnF1+mPz00G/z2WRFmLD+Hv9gur/7pMfLZofERGltaOXbWMSQi8PTKJ8hm8igBa9d2I4C1Ta0sOPIokukkKx9+lLzI4ZfZN7TNmsv8pUcQ9RTP/+F3I57NzLkLmbdoKYHv8/xT4Y7hEWvX43h5OnblmDd7HgtmzSFfKPD0K38ZfG5AUuVZOqeFRdMbCPwennp1JRC6OIqisHHYvPnMnTaNnnSaZ1Ovjbj+UQsXMbOlha6+Pp5b9fqI8mWLFzOtsYmd3d28sHYt5XtXGjhy0ULO9NKk1+/gT68/N+L8Ew87nGmxOB3du/jjtpHDwenLlpFYdjTrurt5PTXy+medfSaxWIw1q9ewevWaEeXnnX8utm2zKrWKp9dtGeIeCnD5xRcD8OIrr7Bh06YhZbZtc8n55wPw/AsvsGVbmHXV9zxsxyEWi3HB2WcD8Mzzz7OjvX3I+clEgnPPPBOAJ595hs6ydxOgsaGBM089FYBHn3ySnt6hwmRrSwunnnQSAP/36KOkM0PfzRnTp3PS8vDdfvCRR8jlhpqfzZk1i+XLlgFw30MP4Q+zJl4wbx7HHHkkACvuv3+woFBANjezZOnSKRv3Hn54qMNdPp/nxBNP3PfGvTLOOeec4ri3hpUrV44ov+CCC4jFYrz++uukUqkR5Zdccgm2bbNy5UrWrBn5bl5++eUAvPjii2zYsGFImW3bXHLJJQA8//zzbNkydNwUQnDZZZcB8Mwzz7Bjx9DosMlkknPPPReAJ598ks7Ood5njY2NnFl8Nx999NGB/67EnhYaZgHDtQXby8pGExr+C7gR2AgsIIwP8SfXdY8pah/aCO+lUttRoAXYOZEOe543YlWUy+XIZrP4vk++QojZUnkulxuzPJvN4lVQa5aXVzq/VLY7y6PR6JD+C+UXgz0JctkMtm2TLxQoFPsfaCj4Ej+AfKGAEJpAB/i5AOEUJ47iP54foBH4gcKvtB8cBOSFohD4eGpoecRSFHKLSDQ+RSL7LHXFcdP2dtGTfDMg0MXBUhfy6DINAEoTyLB/EH63nu8hpTWgvlZKD+ynl/93iZgKhYT1sflsjcymEKSHlPdFZyA4BDKwKliLTw40WN1heXdkGsn+2XQAmx0H5Q8NmxzoeqRuBhu22iOt8nNOE9lIG4H0B8rnxhtp9dppa9/A3KCfRdmd5DyPddvXjTi/TnvUZTzIbyW5daTA1hD4NHXvgmy2crlWNHXuRGUyFcsbBTTV1VHo769YPtOCIxIJtmZ6eWHbyFTnDULQFI+T7u5GDhv4ACxfozt76D/sULL5kb+d/nSBQAkKhULF31Yhn0cFAX7Bo9/Lj4hg2ZULv8/efJa0N/S3YSl/sLwwWO77HjYKXw6e31c830EMbIF4nke++G56hQL+sP55hUL15Z43sjyfH/P8wrD2g2G/vULZ+eXnas+Dvj4yXV2kOzrwfZ9c70jtWKk8l8tVLt+1i3RjI+n+/hHlvueF5ckk6Z6e0c+PRknv2lWxPN3ZSdqySHd2jloe05rMGOc7vj96eUcHIpcbszyIxch0dY1abtv2mOUA2QrltmWNKBdSQjHujGVZZIsLmErzjm3bA+X5fH5EeT6fH1I+FqJatZrruhcAmVQq9UTx8weADwKvAB9JpVJ9Y51fPKcA/EMqlbq17NiRhEmvTkqlUs9W2ZdmQkPHG1Op1Jdd150NbAEuSqVSD5TVu5TQPXR6KpXaWXZcA6RSqWr0bbtV75jNZonvD4FilM9AJks/Cwgoy1egNeQC6CoIohLwe6HzVfxM/YApQaCqS7SlgKDCYw/Q5P8YwUn107DsLuT0DLa3k4ae35CLHcHWBePscAUK1duLigy64BXyBZTWWFZ19gBpGWdrZCZ6jL1wISAWZUio637Hoic6KKMLJUj4caQWFDwG3rLAEmSjFo6vaO6pLjNnPN3DtPb1iBHPLDRGTMsIGRlhuu8zTfWjxfYwkFOp1kBEyGoJ2/WcMSw39MD/DSGX91i1eTtRx2Hpgrkjykv98QMFGmLx2EDfpFeg7rWX8JN17HzrO5CV4loXDXRbxskvpdB4U+RZUcgXiERHunX6KKYRm7QXyN5G+z4jfJynmHwuRzQ2fhwPQ5GiobE9Zw6wW+aRUQeEWjQNNwL/AuC67lLgu4RbDWcAXwM+VEUb24CZw47NKCurilQq1eW67quEXhIAHYTRfSq1nQd2YZgcsuxVkd6AP38JISBuQ48XBmuWwsaKSLQfZmsus6Uc/1JQ0UBNIsg7EPhtdPW+DzHTw7a308BvsL1tRLyR6vRylKxHOc1oyx6I26CCgHwNRmxJleXQ3MgV/AiqiOp8yEJBPCbYsk2RyUKhAJmoxb3L5hPL+5z0+KbxG4HQpWXWyMM6EAgLttXP4EXnCGb5Hm6hj8DagkYR070kyKGUCrON1oAd9JFJLB0iOFZDXybHk9vTJGNRZhx25Jh1CwWP5uZGZEmgC3zqXnsJK5MmvnMHMlL52p4PxIYFHrNtaGsdOCiZvEtnCYEc1TbCR+33QoOw7QkHaqv6GoQpyA3Vo7NZtFJ7PA9KLW/CYkKNAMBbgIdSqdSHXdc9BfhFlW08AVwE/GvZsYuBDePYMwzBdd06YCnwW4BUKlVwXffZYtt3DGv76YnaMxhGQw7mpRheokvrXQ2qAMIK02VbAu2BEiBEhBEZpwQD++pjXBWiRcljVQS9KoLHQvTxNjZdzF37wXF7vqPtS2TEiYMzigIRhNsuQy82LNzlbsDzIB6DObPCZ7Fhk0JlAoTS5KI2gRSDLpsTQQIBJIpbKttsh212C+FuHaA15xaeIEF61CbGwgoyKFldACYtHBASSw5uA40vRaqh6assGxWPI7NZmu6/p+b+Fk49heDoo8atJyRYFVK714pEUEARnepYETAkv4fhICafRzvOHjXErVV8LPXsLKC0DbAFaK3y/G8AT7quewPwE0KXy48AHy9VcF33JMKJ/z2pVOoZ13UXA+8B7gV2ENo0/Avhb+dHZW3fCNzluu4zhAaQlwJvBS6v7RYN4yKdUH1QYVyVFiglwIqhYzMR0kOoAFQQDnSqgFb9IBvLztLogkZExx+o7QUBaosHhZKBhKBzy8dITn8I0Tz64Gx57TjeDhy1BqyTB/trS1Rh6CQhlEL4AcqZXETC8ejcpbGscFsy4gjiMehPQzzvk4k7ZKIW9dnJqYV1AC3pXcy2t5OjgVAtIemXFgUh6RX1ExIatLCI5DdWVVfoAM+ZhhediVUUFgOlIBhn+yXwYNhgmF52PLE1r4+5aTi8SOZz2H29+Fva6V4wzvpBg7AFrY3WpGVGG0GOgPwUCw0SwXTMqvygR0qCzs4wuFhDAyTG2ZObImoRGl4EPuy67grC3BPXFo/Po0ojw1Qq9WwxnsKXi+dvBz43LEZDAnAZcOajAJwJ/D1hps1thKm5P1iunUilUr92Xfdq4LOE2yXrgKtMjIbdgLQhUle5KFBhgDwpUE1HIyi+ZF6aUDjoR3W+iogOqmy1Bq9z/C0CASTrBFw4aKijegRd//t+dvVfhfX20Se/+l2/ZvqWf8PWO1ENg30PvAK+FIiyhErCD3D6J7b6roVMFtZv1DQ3weyZAscJM00mc0WhIW5PSmgQArQDVqA4wRvMISFVGy/bR7A6EiUjJrYPGsjqByihPIQKv7MBoSFQ425thPE/hk64+aWHkV96WE19dbZvpfGB3+Jk+ok4Y19TK42nQOnBmCMTRSCITbGtuUZTmGIhxLB/UkqktqejktbyRv8zYWCmTwD/kUqlSj4nlwNVGTACpFKpewm1BqOVP0LZGjaVSm0Czqmy7duB26vti2HqsQQUKq0Ci5mXtXRGLAVLyaRqN8gDkSzmgOi3UHfWDxa0BIiLMwPzku+E5i71XfcNRpcE0LqoAi9dV7Kj6Z30ckFN/ZgMJZOKpkaorxeknICdwFPLZmDVqHZUaujjnbc9zfJXOhFlt6jJkyy2+5p9KKvsJYSJxzWH+atZGIz0epgMWlhIHWoVSrYTSqlxv2+tpybjZZAM3wu7o52W//7J2JWFoOeY41EnHLlPWiKUvHyGpnk3HNRIGXq47CFqCSP9uOu604H6VCrVXVb0Q5jgpqjhgEMK8HUYSjhWvlQTArRCWA7adtDZnSinGatoYCUcgcorwtltKFqHi1JRYZUobJAzfdR2e3DLAmC7Dd0SWkIJPB8/nMBqxgq6BqNLjkJT34N0xs9DeFmE1gRVJE4K0EQsZ1y7jEpks+D7GtsW2BYcWsjykk7iSUnNQ8GwmW7dnHqOe7kT3afKhIZ+2hJZrEiUQAgUgpKLyyZmsSC9CZGcwj1zIZFBlnjm9XCqK4Yhj6ZXISs8L4EiH50HWPR1dwECx7Gpr0+OqFsNKmrhNzZh93QjCmO7kwEk1q2i89DDwqcSEeMamvmewN6DVlMeIFETi2YpoDEicKbAZsOwjyDlbvduKacm3VnRoLB72LGRUSoMBy11jiBiQWdu+OQfzhRC2shpy5DpNXjpDFrbYYbKegurvvLaThUUQZ8adYh0Lkhjq/DcDAr9fwnYZkP/oNCg7EbWH3Y3UmVGnF8yIrKCbhasupKYaicxb3Z4rKcXhDWur2gmlyUIFNYEBvJAweurNVKG/XDo561WGlWD1qWhHmZMl/T2arbtCNu5/5S55CMW2TqLRL58VguI557lwpyDcOZhy+n06u08Ur+UrIyjfYZoJqYC32qipAOxpMAPNJ52cCpsUVgqjdA+kWgCrTRaazyvgrFqtUiL7ksuRYyzGpPpNM3334ud6ccuevsIpbHGGSW1CEOo7ynCfLN6Qu9aPgi3XgwHEHs4UmfVQoPrugnCkMznE7oyDvmZpFKpvZ8tx7DXEUJgV9AWlA/4QlpgJZH0DqjSx3rvQxX26LOYECCLQTUtwGsIENts9AsR9LryVzyGsuoRRxYqGk36diNKRLGCXmZs+SJeZD49sbcPqjrGwJKycqCqKtGEwkMJqTSylhAhWUhocOKaWDF8QlPgswOLlce2EQsq73m2bhcs2gnRaDOgyVlRnplxOlpAJlIgsBRCBtQ1bEPKkfc3tzPDnK4q/EtFMT40YAmBj+bJVT0D3hQl6mIWy+dKRNE7R0iBQOAHQaiSn+gAaUm0NXbytMCx0YDMZGh48g8DvvCFIw7Hn1XBp3Xg3phSAWs8wsuJCWkaLKHxlSZqtjYME6QWTcP3CZNA/Q+wld0c9Miw/yJFOLDlyvMHBJIwGUUAQiJEFBFk8AsqVFcLibbDvWdhDR2DFaAQlXYuAPC0GDCyDxB4zYoIwE67ooluoECfOVLjgIB8bBHx7GvUd4chnPvmHo8fzAUlUdKCUYJACSEJ/GCEtf+eIp+10Frj2IKmomPKbFVgB1HW149utDhTWyzaCfWFKA2WoteyaCcZ/rrzg9sBvc2aoK1nxPnbG2PMfrLKeBJFEhFJ3lds6KgsbMyvq6OhsYDyBwUdVQhQabWb8x8IgvoG7L5eYmtXDxy1urrpOm90J6zA9xB2jf0SIGs5R4BMWAPSdZqgmPukNnwB2QB8T9Ho7N7YC4YDk1remsuAd6RSqYd2V2cMBwZCCFpjw6RKbYNKQJAJV+1OIzpyTBiYUPuI/tUQCbfmynMhQKj+9YTGkpUnZE8GlMwdFOAf5uElNdoT5IsNSAGqRxK8EMHqs4iO8up3zf8amfTz1HX+D9HsSpJiF9nGoxEFD5HPh7m3h1FA4zsWdXUT23OfLL7nkfd81m0QRMoyfB61YxdNjdmK2xy2BQ31gkJBs6muG1s7HIpDr05gBxHq/HpyMsvGaECPnsaszR6ztg2VwJ47so18xMYLBHYNCabOWtRCe/9Id8uVO/rpzvlks9AY9aE8+ZgHOhuMjO8xxXQddxaRrjBnhPB9Gl59DpnuQ/ePrkXSgUZbtWuZdKz6e9GBRsZDadpC4qOYyC62FlBQCr9g02BPQnNjOGipRWgoEOZ+MBjGJTLC0MoKRyxNuLFl2eAUl8RaQ8ECJ1wJBYVhuwEWCDRytAFOgFNWFLWBReGQ2q8EeS1wBKg+QfqFCPQIWBu++taMAJksm5wiM/Ail+BnXwmFhr4nwhWd70MuFBqU3YoXWzZwigZ8IXHsvWdv7/k+2Rxkh+QoUrR1VbZRtm1wl0j8QLO10I8Q4AeQyYAQSSLOMWjt43j9PJ+cRlBI4uSHutnG2hPkojYvzFqI4yvi+YBkbuhU1uj3UjfMjqQ+blMfHzn0bO7N0Z3z8dEIqYb4PAotwB7fKHGy6IYG8g3FnB9aU//an7EKeWI714MQaCdCYfrsIS+o8BSiUkjrsfAVODX4PygGpHA5wa0JINRYWGFC1KlwKzXsI+yjwZ2+C1wDfHI39cVwoCPCLJnDQ1ADYewHrxchkihtIcustbTW6EAT5BlMflWGUqO7KUsNnhIgNDquw39zktwjYXAc0RoQvWKkmrzgzCEJxLvvJd490kO4c9b38aNu2EbJn3QvUmtEON8PozLalmD+3MFnunaDIpstJikTMRpFqD3pcprpcpqHNrINIsC2UgxrGxgWvsNRHhd2P1yVfUakuPWTDwRS55BlycCswMPPa+wKQoO07apymtSKEnGCRBI73Ufjc4Nuuj3Hn0F+7qLymiMjpArJeIYO2odqAztqpdGBQkxBJEgBeEFAb0EwwiFJgGNZRPdwaGLDJNlHhYYFwNtc1z0XeIFQ8zBAKpUaP4avwSATwHBVrobYLCh0Ib0+bKeR8gFXS3AaLfy0RhLaPJSjdMVdAwBiWqNLqaclyNNy+FvC195b56C7JBH0CENMv/lisoX1qKA/HKaVQvg+tvc6tr8Nu7B2QGgYuIe9ZFwmmFjI663bNfV1ocARjwuiEUEsCtmsj+evQ4p6mnUzbsGmT3czPOxz3rHoT4TPMhexUFIwrStHrBB+vzsi0/CkQ05GSagc41HSThUCkDpP1BtUbDpBgOrTDN/U0IEmmUxMuZZHaI9CfCn9RxxPbMvaMFNpuhentxu7u4P8zMFkW0L5oZ/xYK/AdkJ/4FEvINCerlrU1AUNWQ3OFAXxUYqthWFPU0NgQUsswty4ERoMlalFaFhCKCxAKECUY4wiDdVhV7Bg1xqirWBFEX4fTmT4BCggJikIDx0wwugsEAHWKJH+LMLV8ADLgvAP2PQfFionifeDTAyPONVIMPPTdCmB1AIZ+Ii+NHW5H1PXcye2vxlRzOkQWrMHCCy03PPhfYWYmLjS0ws9vWH67+nTYMY0QSQikEKD3o7S27HE0RxdsPD9tWjdX6GR8J9nDmtj04w6ZuU6mN8bJrx92jqRLruZHqceWYUfeSkqZyaAfGRkWvBK+L5Pzo4ROCOzTAo09gTnWBn0gvYozJhDYUaYSTC2eS3OX57CyqSL+UooJj+zhu6naRXKV2PJMVaNoZn8oqJuivYT4hWkbK00ARqv6OZq7B0MlagluFNVURkNhpoRxZWysIoDblETMdzNUQrwiiEPp2BAs+s1hRzs+EX9+JUBaELOW0zdTEj2/BfJnv8aUaO74a/par5m7GameDAuRdOcqIZSa41XDIzV1iJoaxns37btBdIZsO2xM1KetgZYAzCDVxb08+dF/TjrLawu+FNyeVX9yPesBP7Ia9v7WXveIuTc6gSHsThuQz/HbJlA7DlhI4I8yEGpI4iFXiixbRuJbQu1IH5dIztPu2Dod6pV8a/SlsUEERqURu/GcMFag9AaXwUUPDFo71C8t90pRASBIpiEy3KtlAytD4SompP57U8E43Nj2DeQTmjXYEXATwNF4SE6uI8uhELrktAw+Vc3udTD65bVJz/yIb3jdPzZs5C6a0RNqfMkMk/T0XzNQHZGe/igtBt2MaQQSGkRqIkNukoF9PRbTMuLERmQE8kOsrl6tB4nZwPgC4GtJPM6o7w4rxfd0IvuTYT7S1VgtQ5mtteberBmVs5vMhpD5m0EgSXY3BKZkNCghY3tbcf2yzxGogFBXRxZtPkgUNj9PcR3vYioL8vhoTWVXirfmUHgTKu5L+G1NKrL2+07YDrQZNGsKj8mHIQtK9oTTRW+H2Dbey6qoWyywJbUY4+a1nx/QfsaS4wwJ9pt1BLcaR1jDK8muJNhUtiJ8K8UmSjIQdeL4JRpAaIeFDxg/P3xamg41qfh2LEHqt5AEBStzHt/FcPfMYed0+/BmVO24svmEH2dtG56M06wgyZHEgjorxDqVxX0QNCgKUNI6usmPmQoFSClxdbtQ4/39vdhyU6E6KyqHSktsE+iMWfx1j9sKB59pep+dFmNPLTkOHKr/4z7+i6Oy1Q/ifieR0N9w8Bz7Y9I/veEafRHJzYhaBEhsIdteVjQc8oJAx/r//QiTlcPwrMJrLG1IkLnESo3YW2DGLn7slsQDJt8dDH8uLSQVWShnSi+p7Fr9UCZIKqgsZB4djgB7u+BrgIVKmD3FLUs124b9tkBjgdOB745ZT0yGACEw3AZVUiKuQvE1E+8o3WjrBdWvSbYAZnHo8hYWd90FPw6muY3Ylk91L38CTQW9eiB4ai3/3L60m8O41IAIqqQJ/QgIvuuOZAlLbRSiFECWg0nFD58lLKpTx5JvjDUqDtQ7Sg1ekLchMoi7HDyLYwSwXI09LCooYmCQihNNmJx/5HNI+rX5QNOXdPLKKE/qkLFY9DVQ/L1dej1m8epHb4LSr4+8QvuJfIz5pBZvGwvmvpOLUIU414gD5x8ofvi9kQqlbqh0nHXdT8NzK1UZjBMGGmFWxb5TrDrwYogLIn2NcoHGSnLRaCDMhW4mNze8fBuEEacDDToNgWrQXVVGmxsci1HkWx8gljsLyNKHbmJ3nXvGPisAaankfPTTMVQrAFdRWKtWrCkIOf7SFV9/+xkPwXVRMFrGDBVKSFEjMIYQkNEF5DFRA/5Cey0KK0HXC8l0JL26ax32NE4cpm+Azh0R5YZfRPPDug31BHdugM7kw3znFdF34Svt7dwunbizY5DToQeSBNJtCHE2FuKvo/t75ndch0odA5kL+QBNQVurOVIBLEpbnMslK/Q0SQwb49cbyq+pbsIU2N/ZAraMhgGaTwKMpuh0AVWBBmRRKZH8TpgiIO5yhe9MgLwx89iWAsJqYkWl6P6uAKZ2QHZwtDLo0MjtT79FXLeq5TE/lCw0LR1/wN2ZCf6je1YOo5eFUGvjqJUAt0oJ60xEUoje6d+MopFo0QitenFPX8dLS0x4lFNf1rS0yspeB6OfQRCRBiquxmKAGJWmDI3V4OgUjp3uDXYhSu72JUcOcS9ODfJtqYo/TFrUkJDfu4s/IZ6tO8hqxJUA8R+trZNvrga6fnIXBodjcD4aVgqoIseJqNLglIHiAna5NSM0GFIen+st3FihM1qojh7TCujcxkI9sHU2GNwHIxwnzYYJo8VgUgT5MPVqZACGZVFy+dhISMpBtPRhSnVoQpR9iMRkJgRkA8kTkW9dhzNoKdAqRvqpRlYha20Wv+JsBrQ0yxUvw39CrFSTUl/RaE4Ag5Z2mto9obEtdAIMolT8J0qViVCTihoku9liTd5SEtgWRaBH9Dbn0frGHXJuWgdkBvFLCVe1DTs6E7z5/wRNDa1jqij0WhdNJYtopQiYjtISzIz6KJJZ4gEmpm9IwfTLf0+25qirGuLkR3FVbdq5iTRWiEqzKRRX3HIzhzWvrsDNS4qvgXp9RHbuBMVLQqQdq0+B8WNjTGkDaUU8gAJKBWgsSaUg7Q2VFsrNDaAo/aoF0gthpC3DjskgNmEWS+/O5WdMhgGkHZoFFkIAwKEK8o8eBKc4cZnouiatvtWLFKH/uxeNT5OxaiAfnR+KDRsv2OwbKo1iaMpBHpHHkqmH2P7tH8f+CyUCsM2TxF+MS16NKKJRkJjxoKfJ5uN4XnhTuZwL40STeQpWQe8/MrzNJ7zTsR4uamHsU72c37mmVHLk+k8kGRLc5QtzWNnvpwsloJDOqbGcHdvECRi2L19xDZt3dtdMQxDxjfhn3fmHrczqeXXuJShmhwFtAN/D/x4KjtlMAxgJaDxCAYsCAEyechsKFp2D1udWNHdGlLV0tAgYPz8TD6lFVZu3j+iOn+LIghtDzSoDonOj/5z16HJZ/UdG+bmp/MS+i1Eg4+YU0Aj0LpAc88vcfzNqMSgi2AQBKOH1JwAOTQ7u32cUmQlrXGcjSjdhmNrbAv60pDzRt7fEbZN/9JjWP/6i6ACFqR3EImVuzNaSBFD6SxaDbq9ah06ua5LHEKfTJDJZkd9etM35jhCKApTYK2vtca2LOxhUtDOOof2xgg98f3bnS+7eEFo8Dnp+BBj/2CGJ6nbp9ECLDlq3AqNRqFxkLtN3yDXbURkc2ESkeI19xS1GEKevRv7YTBURtqDbphFRCyPzrYjtD/SF004u93Eu6p5QBHuM0oNdYdA3T8M9QZfCIGvwipy5Knd+DiTMKbyt9pkfluHiCnqTunF04J+BY2992KpXhy9Di3CVbYUAbJGX3UvOm9Mw7ZexdBNSwnE0iRiWVoTGbQjyKdHnp8Azp4Hv9rs0JPxmOu/SqMe/I5lECeuXJTK4vsjU3Jvic2lICP0xuYSVaPv8y7YMlqJJqp6cEaEOq+MHwRYlkUyPjT9+KrpMdobI2xrjHDspvR+63WgEnGySxbu/usU3X73eUoLEtsZM7W5QuMjSOymUEhyeztksoiOXRB3ILLnItFWdUeu6zrABuCCVCpVveO1wbA7sARaxBFBNyBAeVCt4Zyw94irZngNNeYCS1Chiij/j4mvHmRdcQWSk+SeiOOcmgEsfGcGkcJG5q2+asJtA2TqTmbboptrPs9XoSA0rUEzrWH0Sb0xIejJQMEfOnlr6YECKZNEIseOOC+pLQrAkw3LRpRVS0T5nNf9CHYVgoNEoCq4h9blwnN3NkR4eU6Co7dkRtQx7K9oCECPM3sGaFTVhq+iJs2iTsQRmSz2s3/GAtSJE3/fa6UqoSGVSnmu68LITEMGwx5HWAKsJsIgT7qomqtigtUFULnQhXN3M5CPYPSBQAhdZqRYkh5kMZfFJC9fp5BtPqrDJthhEwUsodnV9k6aO38+eL2ibFK9HKWJ5DcQy7w4oVgZGc8hU7AR+GOGJY4Vow8OD5us8fHVDiwqf4dL8opUNFkWIaM20tKiIG36rQRNwfgeKUKIipE4p/d5SKVRUtBeHwGM0HBAUIoRozU6P9aYo/HQ9FQ5ZQoEyRq0Enr2HJxcAZHPITwfuauC8dJuotbgTh8ltGEwGPYaQoK2myHeBoDO5yFahUGbtwsyq3dz70pIsJNj1hDAQJ4lVYCgMLBX4aDQMKk90fo3pen5j0ZUv8SOSVoEkHgTfXPfhAr+//buPD6Sszzw+O+to1tSS5p7xjO+L14bgw/wGoIJYIjBeJOQDYYsBJLls4EksJCFYC5jQjjMkiznLgkJbGDBuwnLEgjhcMAJXoxJsMGOwddrj/HYnstzSyOp1d1V9e4fb7XUavVRrT7Usp/v5zMeqbqOV6/GVW+9x/NYZotQ9jxU4rrYM7GW0+95AV4yhxdPkwTrOipTYj32TE8wNTVF2CKuRJBmrUyW5W+AUn4fsK/hcZuBzQ0CSapKRDhbJAlb3fJy/Hjy2ewLQoreaKZGw8JDpI5v4cqfHeEbF2xiNv/4WBUgarQYmnAUnURNiUg66muwO7ZR3rEN/6FdhGYnqtlypD7opNGwA3hZmhr7J7jl1AskNbYYFJfpbwUPU9Xb4Ec95aklQywBijIJ3fQ5qDwQWqgopr84sfRUaZIblXY1JLUfnlHBu7hJvAulqOS2k59/kJN3vhqb4RZivZDDJ7yBucnnuFNQjeDYXJhGofyROUw+PMKFpxd43lO6SGBlQdnWv39rK9xz0PVs/LRwAfeQgI2oTwm+/DjbcLmgjRW5uz3mgC91n3trQRBb8tHKJibmiHh2+R7GrKyS707v42N2PPkZsOkkYW++t/FpWumk0XAmcHv69Y66z9bwSmSx5ngKEktcdDfOpBQTJ/HSZ2IC3ljdDGcVAgqiY83PbdMHdbg89HB/VUNju+98FFFHY6LLWQXeiRWSXTmS443fdhveou7KYZ9eajryUCw8jfz8gwSVA5nLMnnk6wuNhqbXrbFpIg/MMFdOmCvDrQ/M8KILu33ytr8hn+gf50HylD2PMh7dhrLx+vBsjoBohZ0Xs8BebxNnxY17akQWFqIKvW00WOaIOp76HPjWrbauDGFwJ0mNLYaFN+IxcsriMjxbtORHRpY0XSuPldwLYm2vuz8CExfROq1lGWZWY67v0hvQKIp8Rx2cjR2+cgb/eOAi4NVJh2aplCMCfGKlKH59HFXyoKhgrHE9Hd7+FqY2v5J2b+AAufmfs/3hqwkqNdmw0lTerWzbuJ6XPqtAHCd87Ue7KZYT7tkTrjg9s0oSgplZkrB5nYYh/MLJd/CkuQK79k0S+Kdik2kq8YMtz12OKqwbb9ygiRXMdxtAqsb3zllPOfC48qeHGYk6e1d7wN/O/eFJzKr+xqZ4/Ov9cNOKm6YL/zsM4ZJLIYaFUmpJY0D5ank8/NDDRkk6lFHDa/NPvt2U6L5SS77uxa0pUB5MJi3nRtj5BN8qPKUojlsogf3RCHak+Y2ozOmZrh+xEcYhLO5m008+Xr0iG+KkaU9GbMc5PP9qQlw0SI89JNYS3Trpfp4VsL6lckKZpMUc2ChyUxQ2jc2QrI+Znj4F5Y1g1bbqcvjG5fUS8nHzB/FEozkWagqlZjr4CZyCHaGUD7n7tBwjHQ5RzBRjmIL7wpMpeDOcVT7U8fUFfVp9tcJz9jDPTlbSaBCPS8oHu5IeO6Vc7IfKYXcSf3JA/2NWl2h2+8awNFxvgKJI3HIOd0VZksStQEnWxfiHfXioN/M/ErYSXbiJIDzM+tzfZD7OHtjB1IHfBCC/JaDoV7APjRAmK88RnZTKoJsnzAIol0JyeY/NG8scP55gbQhsbxn7yvc7/7VZO4nv39vZQcD6uYgj4yG7tnS+Ll/NQd4FVuV2X3MGhwaYVkn0RbWt0ceAdvWk0SAel5TnLY0i2YnC2S72w/xulwzLH21/TNc8CLu8TpK4ctcYwyPf5i2mrCDMu1vByLNLlE6MsdHi9I4YRWQV9Z02YCnTuhcDYK/9b4zaO5Zes1JBKW/ZkWPqDtZ538M/eSflE90TLjygKMYw++SjBGHnD0tvKiD4eQFbHiUqjLXcd9cBy/ioa0CNFO6jkB8jiS2PtXgpj+KIsZFRsr4tWrsDyGGtQjUaN2rh4odn2Hq8kjksSb19GxIOHj0di0dR5SnYwU2gE/0w+LBh0mgQj08+K299+wV3fDQD5dZvpr3V5f+OXuTiW9fcR3xU+2WbSpFLVyvkJ4DzY2xsqZQtngdlC9OxR67Ba2mF6gTU5teocAYVzliy7ejUFIHvL+vpXTdTYN2B75Efu5vR9d8D4Oz5QxwvVqhMPMJcfmkdhb7HxGjYssdYTQbkDq0jiWOix9rHSygBkVrPvPck1m2eRikYOda8ARrFMfn84ozHxCoqUfMVPjaeBUIStRHVIvPjEhMJqmAZKcOT9rffvZkT8zN8Z2yeuDTCfSMnU6Cm0aAsVpU4bfYoozQYUxFDZ+EONyw9DVrr3wK+ZIyR5qhYU5QHScVCo0RMqmZYUqnl8x6q/BGIZxsMTyjwRgYTWbIjHmBX3sNSQ6VBn5I0CWeioNm8u3nAS29f9Q0UBY0zZVpLFMfLJjYWvW0AjM/fxfj+dwFwxjgwTrpsoMG5ssS10enfHaxmePTeL7Jv5umZ9i1mPy3VQE+WXPbpa4GCKzej2sYHaK1QhB2Jz6Mh7GT70g/TGGk7C7P8yuztDY8XQ2YV7kHtXm0+B3wbOKi1joHtxpjs66yEWCVezieYDBq+7NkYqpmj4mJCMN5kwNovQH778v8xoylIii6Z1lDxXDrxrI8imzRPRKTAD9yZksgl/W5Ul6MowvR6FSx+3VrzklXkGixpHxkdJY6iZaecDc7gwMRLGIkWk0OUKwmz85VlP1WcWKI4YWI0ZHy09RwMNROgytlusPlwF2Gwj9wJP6U092TCmtzWCly7rC7mhUrjNCgFnrJUKoooapbQKADbwdyMI4lrsc1OwbruZyGcpMoUGV8Sm0NZRRB7PBYEzCVjJPRjjYDomwEGPWjXaDgIPAP4Bt0GwxdigLy8R25L66VlNrHMP9riHdEfhcKZy7eX9sH8kKYK9jpZTpeAbdJdrxR+bjGU8zi2xUuN+2AGS4xd8rCpJKo2/MSC0Xy+YRRPCzy68Q0EfvtH1s69U9z2wCFOP2GCZ56xte3+WW059jfsOPJZvNN+zuymo0s+8yoRwezckqWbpUqZ9ROTKOUxORGxaWPE9HGfw0d6M5k0vnkDdv8I3vx+vI3dd/qeUnF/alkbkiRP5uuFccrKY17lJADUWjCESy4/B3w97WWwwJ40B8UyxpiVT2sWYhWsOBWvN0KWGAXDz8t0r1He4jBFKwGKSl2jodO3VeV52CjbeHphxD2UH9p/nPlSxHOfun3FcRxqVQI3RLJl6itsnvpqtoOOLP12EpZmx/BCOOv34YTLOy7Pga1lpvePkNyyYcUTIDNeicKLc5Q3j/D3uUvWbGbOLGz6D7Pac7RWbZ7fz1X8K6X6bL991LLRYIx5p9b6q8CTgC8AbwWmBlEwIQZBeS7QUEcPGy8HKKgcbfBhAuGmXhWv/zwFlRiS6l1ULZvDoTwghqTtzVVhsVSsXYijEUNHXd0JCVHGSV2TE3nG8gFzpYh9R4vMJ5agwfyUTt/3Z0aeSsXfQBgfRXXROFxSkqQEh26B7Z03GgpnznH8nnFslK2R140TH57h6MY8eGotP0szs7AaCxB6JkmHxaIWaep7re2VjDG3ArdqrV8AfM4YkyGLixBrhK+WR45se0wBJhtMkLMRTP9rjwo2IH4BggDCfJo0q7LsJuoHHr7f/hGSAwo2oFxabCS43geVeV1IzsvhE5PL8gsJfV7+jNP5m3/+OfOVmFwEhZqACgkwu4LEvFGwmXtO+RKtI4e6z4KZIuVKicnJdXhe9dpLj8uVd3LivjdQPn6QPQ+vIBpjkOD92v6BvBGfY/fypDl3qdFRy5ZNlkceuQiIwbu7b/PubJIszAvpq/vGsfdMcPhpJb7zmyF63xyX7Fq7j7TphXk6wzM8scAY85p+FkSI1aDSPBadH9jo7plOvFxByujVlZa1VV1k/HncQkO7kG7bq9mehd/hg0MpRT70ma/ElKOEQs0zuatHULuAXukPZIMwjfvvUZPnfIkoOBGAsLKHrY+9p5tSDZTnWUYOJ5w0tx5rFb5fwfdLeF7nyzErFUXScmyl9wmgGhrzsGeG7Ji1nH29Qlnwerxc0U/AX8k9ZQU22AngYsJ4tu2+vdJRn4bW+jLgGuC8dNNdwAeNMTf1uFxCDITyIZprEG56peer5GH+EMu6LpQHQU1+AmvxQo8GEZNWkZcmpOhuvoYiwVpv4VRl68JHoBYyfzdVsVCKY3zPy5yuuxpjotwgrLIHNAsMGtD9Y8r6Hl6UQKWCavKgsDZP5G8liA9QKP6wyysO2Cy0TvCezVAFBEpz0bWIKL5mxKWtzHAxI/HhgV0z8+9Sa/0K4Hrg74D/km5+HnCj1vo3jTFf6n3xhOivcEOOYLKHbwXxU5bHr7YJTN8DucXJStFchC3GqIwPxsHwIex2GWlCaOex6eN4EihUl2SWbNtGg835RGMFyvPz2RsNQbXRsHwoYrTJMEeJuCfvtkkYUhovMDJecBmvmtg99mny86bLqw2e7yUElYjEjlGJtgEJI/ldmTvSfN8yMR5TKiuOTTWvnyRJGqYX7wc742NLHsXQI+pxo/2RjXkST3Henrme92A0kvAY49AwIV2/dNIAfDfwbmPMh2q2fUJr/S7gWkAaDWLNUYHqOmDOUqPpnzrlEfDtQhe2H/tUZpMhnIPVbSPGB1VeWJni155RuaZEyweOgrHAp9RBzeQCd4VHDs0wV87WdR5jieg+FkHgK7ZM5og9jyBofjuNgx3Mje7o8mqrw586TmIhCc8HfObUbrKOofueJclFJAFETYZvAKy1JIMa0lvv/sqnfzpVLoO10yi1vGfrrgs3cWwsYGrPLBc8OkPY50VW0e6djDPYNOedNBrOAr7cYPuXgcwDdVrrK4HrgHOBfcAnjTEfbbH/euC9wOXAacBx4BbgGmPMfTX7fR747QanCI0xEhNVrC4vD+Vj6coLUHGMLZewtr9vV8pXy0MyqqC/+TQUkMTUv8crEmycBkZqMWfAQ1FKEmwlJgiaP2iqZ8+Hbp/7909z//4s4SF76ymnrefcEfeGCW6eRbUBESwp6dpkfQ9vvkzil0CNYe1JmY+NIjj4OEymqex+PLtn2faJuQrHxgLuPrFArhxzwSP9m2RpPbU0yNiAdNJoOAicD+ys235B+llbWuuLccMb/xV4BS5w1Ke11nPGmE83OWw7cDquYXIXLqDsB4B/0lqfZ4ypXfd2M/Dy2oOlwSCGwthJLpdFSuUsgRf1ddKzrSTExQQ/X/OAtjGU9va30eDnGsbC93NJujl2gR+adEdP+h6WUWbm58l5atlyWJssnWt6wUnr8ZUlit01I9r3l1i6j7QxXSxzcGqeuWIE5YhSxd1qFIrC+nXEWCp0vuRz2CT5HMrzUMk+bLKh4+NDP8m0+mbYKQWKkLnieqxXIAmXx0a4cG+R/evzVAKPoxM5kpH+xU/w5uZrGt+Daz100mi4HvgLrfUW3MMZ4DnA+4HPZDzHW4DbjDHvTL+/V2t9HvAOoGGjwRhzL/CS2m1a61cBh4BnA39f81HZGNNFOhch+iS3zv1JKSDs80yspJQQ7yvBSG2jwUK5z/+LqLDhPcyr3j+TMsTlpo0GHxjJjeDbsluB4dU3GlwyrWrQyNFCwLYnbVn4/GjsuRtbn++jjx6e4capvcyXYsZzix3d5UqFvFVESlFheY/LmhOG2DAELKo+ilUGzVKG1IrjOPMcltVjObFQZG7PelB51Ojy+T8bgcsfKvO9U8Yoh3nmx7M1TUdi2/G/Eq+SLIZsH+I5DT7wCVzjWeESwn2S7MMTlwL/o27bDcBbtdYnGWN2ZzxP9e5bv87kEq31flz+mNuB9xhj7s54TiEeX3wAuzR4lVKgci7YUHWM2UaLIfIy89ovS2x6qOeWU7SZKOapxqGVFlamNzncwxKjupocliVY6HgakXKmVPdItKR17i1uWOsNBwEoVHW5aZIjnmoQYh7YOAUv7bBdvpJerxiImQV2D/RfVydxGiLgaq31e3DzGwB2GmM6SfC2Haivzv01n7VtNGitfeDPgNuAm2o++gfc0MdOYBsueuVtWutLjDF3dVDGJUqlEkmzpD49UCpJAtFuSR02ZhNLJSljjyZ4hcX/1VVSgOjoQv9+VImwneQZsBaVzEK40nwPMcRxmjmsxV5RmUrCsgmG1aWczbJuqkRRYeWP6chC6LVvOIRpm6AcxcTx4s8S25hKHKOSBFTMPBBK+qeWkqTzIFyrYbrkM144SHF+Xcv9ip63kDS+neq8hIm48zqIa1rGxWJnuVZbGR1tPnzZ8fLZtJHws24KtFJpg+ELuLDWzzHGLDzNjTF/XbPrz7TW3wfuBt4EvG6l18w3SKrTa61+QSIbqcPG7KmjzO+eXzqvIX/20n1Kpc7+nVsL0z8GP+wiiFX731e+EmGjCmGjVQktJgqMrLBEACSWI/OAUu0bDWmxkoQlXet+YvE9n8D38Ak4TrwsZbhYbviHJ2C6XIBwyv1poZOnxv89Z4xi6PHr981SqHTWPeYfXuxsH9Q9cNAxN/YBJ9Rt21bzWVNa6xzw17iJl89tN5RhjClrrX+MW3EhxBOTl2aE6DS/RitKuQkKNnLzF/rE831suVlopj5R7qZYpv1yTD9tVcTLxklcfZPhHEIUKpZiCDeePtpxJMnzjubZAgxyGcWgGw23AC8C3lez7Qrg4VaNAK31GPC3wKm4Hoa2eYnTXokLgH/uqsRCrGFKKVTOIyk2T1OZlGLi2u7hBPxCm7c+vwDlg5BkuYXYdJlnZzM/lRcQxRVsOmcgnxtAJj/rgnRGiU9C2pHSpN5s2giLE0s5WWyUVazl6OwseIrCyBhxoIh6PFOtm56LgGxzNsRgbJ6LOTTmM53vvIk5Hw6+WTroRsPHgB9qrT8IfBG35PKNwJurO2itL8ENQfyWMeZWrfUE8C3gJNwqikRrXe2tmDLGFLXW47iGyFeAPcBW4GrgDOBVA/nJhBhSua35hTffRmwR8qNpp76F0t5S+56J0dNh5ORsBYjnYO6BDkrsjI2OM5IfIbGWw1PTbiln3yWM2RIjypIkkFRsy/me1XQdEypZ6HmwOR8LlMslJgnZ4uXofG58YxaYJiKkRWumhZJVzFslPSBD5OJ9Zc46Eq2os0DNDn4uyEAbDcaY27TWv4YL7vRW3CTIa+piNIwBOv0b4Om4pZUAd9ad8jXA53ETSZ8MvBK36uUg8BPgWcaY23v+gwixhihfLS7NavR5oPCCxceIF2bI/Kn8pgmaGuzMSh5wSil8P8QHtxJhEBEDrY9SbujBwxI1iI1VK/A8ynGCtQl+tT7SctrAx9qEngYcBXItf5utBeB+t9LTMDQUsKG0ssn2C5FfhnF4Ih0ieDvwS7h5CEsaq8aYM7KcxxjzTeCbLT6/iZp/0vXfNzmmiBvmEEJ0SQWK6HjUsyReWA81H+NCq2TkjYO3OF/CVixJEnc1L0MFqn2CsGr7JonThKWWOFZNGyy+pyCGcmQJ6l7frfUoliIUy1emqDaNkVqe55GrmQzqASWsyybaoQgoq8CtEGmyJvXxEMXyiWO4hyf+HPhlXI6JvQw0nIQQYlCC9WH7OQ2d2vAUN3Eyi8q0my9Rkzwrp0KU8vBX2GhIIkjmMjaE/PxCEIgwjwtG1eS6QTV0tAdhuHSfIPAoRxGzleVL4eKyxctQxRbwlWLLusUlfqP4rHSgxgKjCmasImiQwb0CVEjS4Q8x7BYb0UPY04BrMPyGMebGfhVGCLH6vJwHuV6/wWzKvmt5BKaPQm7x9hTMhyQ2wVvhsjwVWZK5jDdW5S/cg5UConLTbgE/bYTEimUNEoViJGj8eK+QuDhbGYo0X6ksCZu9JAlYpxTkFcSJ6wmpP49LJS7vg2tFMqDMoLU6aTSUgUf6VRAhhADcsERSgXgxaJeyZaJyGRo0Gnzfaz9skViISk1DVzekcu5JnZAm4Fqu2tMQlyPI15atTYvAuomWWRoNNraUypWWybs6lcSKklXLHgAJEKmod8tzM4qTBLsKjRVV8996nlKoVXgod6L6exrW1NifAn4X+MM+lUUIIVwMiJGtSyJGjowWsA1uV3EcUY4suVzreBE2sKhCutQhi2QeRRHrFSAI3fhGg2dLdcVEBIvzJazFLTNt/uD1Q0Xc+JTLKM/j6LEZVNjBRIg2KolbSVHPJjDrWXIN0j73UyVJCAf8gI5xwzVBg99CYhNG8iMUxpbnlxhOwzk8cSrwUq3183GrGJbM7jHGrDjqohBCLPACmFwatbKwDgoNdp0rznPoyBTBaPsYfF4HYSJU6RB+8eE0UVPzBkmYD4ESTIwSbk4vEEdgS/RqktooIZVjETZOJ3P2S5oILLCDSfhVazUSVkVYiiQNf7tJYpeEBh9WaqX5X7rQSaPhLBaXPJ5a95kMggkhBs73+rQU0wsz3dSC6pyGuGZvlfZo9LBYyvewcZ8zZlbnTChL8gSI5eC1WoasFFGUceLuKkqGOTW2MeayfhZECCE65fs+noKZ2SKFsZGejcXb6nLPSpscA4Hrxp+bm4Zq3gCbQFysS8OZAMGK86ErP4ZKgo36O2xgyxYSjzJ1jyHl9TVMRhyD7w/m3dNaN9fV+irNFNlYxVrmks7Dr1tswyGPfrCrENpz0BEhhRCiZ4LAZ8e2zex97HBv82v4Y8TrLmy728SGn8LuB5hKthCvO2fxg7oInCqewz9usEGjQZYMxZkAb2WHdiS0ltElUxITbKVIpeQ3moPaM5UoIezhRM9W4vmYZDZBeaplpM6yp9jktQ/tUe8Y8YojdnZqaiH7+hD2NABorS8DrgHOSzfdBXwwDcAkhBAD53kevu/1fow0Q8TLyYlxAO7Z+QhHplx8vtNP3s45Z56yZD/LKLbLoZTMATi7Uhdt0iqsVSQVF4uiX89BlagB/XzgBx4JSds5pcpT+NiOY4OEXUTs7JQd8CoX6Cwi5CuA64G/A/5Luvl5wI1a6980xnyp98UTQoj2PM8jsYOd8Q+wZeN6AI4cO86RY8cBuP+h3egzTl7a66EC12BoM9wxdKxFRbN4kV2IzeX1oUvciyM8O5iO7yS2eFGMjVv/HCqqEFFGNeoBabUyBkuJBJ/lcTB6LqpmgR3OnoZ3A+82xnyoZtsntNbvAq7FRYoUQoiB8z2POBr8bPcTT9jMrzz/mVRi12D53j/fQalcYa44T2FsdHFHpYgLGtV0BH2IJQlqInGrK+YtXh9WcJTLZdQgspji5hxQaN8vlZTLeOsmCWqX81ognqdVFrMxwCNhhpiRfk8nPfzYYtF6OTzXQqerJ77cYPuXgff0pjhCCNE5z1MtM3n2i1KKU0/cRn7ELfm84+4HeOzQUe66fxcnbNnIKTu2Lt7Ig8LaXmZmLTaJSTzV84eTtSVs2H7ZbK+o1mE9nKCEGl+Pl69tzFgoH6dVDHAv3W+WiH73AKiZNGXVkM5pOAicD+ys235B+pkQQqwKz/Mol6NVWfwdRTHVx926iQKPHTrKD277GQC/+kvP4uzTTxp8ofpAKQUKbGJ7/oyyiSXJGnhrQGxsKc6XqR31CnwfPwHiCq0bBJZYRQ1SlTXmofBXkgl2YahoOBsN1wN/obXeAtycbnsO8H7gM70umBBCZDU6kiexyQCnoDmJtczMzlLARQ685MJz8X2Pg4enOHR0ioNHph43jQaAIOe5QJ09rmYvVvj9DFy1ArmRgNnSHLOlOQCiOGbjuknWjeahzfwZD8s6PLLOspkhIVjJUEZN9tNhHJ54Ny5PyidwIdIUUAI+iQxPCCFWUT4Xkm8TSrofEms5cmRxcuO2zRu48rJn8tN7H+S7P/gJx6ZnqHQZJCgMhmdlvB96rQJkrlhsPYL8cIWTCvJL51hUogg8S1jIFlp6YwfXKlNEoVoGnGokmK+WxcMmSWe5VVaok+BOEXC11vo9uPkNADuNMcvzvgohxBOAu8Uv71afnHBBFe7d+TD37ny4q2uc96TTuOK5l3R1DtE9TykqUdSXN/oARdIyakRjyl9sJAxqTk/HTdi0kfCzPpRFCCHWFKWUCxJU9yDZvnUTm9ZPMnV8tqvzR3HMgw/v7baYogc8z2Nmtsgjew+wYXJ8oWHYC3l8jlMh6nBSTsUfstwTWuvvAC8zxkylXzdljHlhT0smhBBrgGL5eHI+F/IfXnZFV+e11vLJz/0t86UypXJlVYZfxCKlFBPjY1QqEeUe56VYR8i6FYz7THsVLAkLwxMD0K6nYQ8szOXY0+eyCCHEmuP1aRxZKcXkxBhHjh3nK9/+fwQDzgI5SEli+xI0aqWU53HJBedw6onbln3meYqoMhzJrHzlUy2JTQYTA6Rlo8EY85pGXwshhHCqPQ39sG3zBo4cO86+A0f6cn7RWuNGg8dcscShmgmwq2W+XGIiHdIYVJiSTsJIPwf4YTohsnZ7ADzLGPP9XhdOCCGGnef1Ie9F6vJfvJjzzzmTZBUCVw1SpVwmHFBEyHZmZuf49k23Mt1kPopSinw+pLwQwnn1REkMyoIFO6BU3p1MhPwesB04ULd9XfrZ47fvTAghmlCqf9EowyDgpO1b+nLuYVKaLy1E1VxtlUrEt7mV4zNzTVdKDMtQkQvulPY0xIMZnuhkMG6xdEutA+Z6UxwhhFhb1BCNxYvuhWHA6EieOEmYnZtf7eK0VPsvb1gmQqK1/qv0Swt8UmtdG5fBB54O/KQPZRNCiKHnex7xkIVAFt2ZHB+jOF9iemaW8cJo+wNWiZuE6/7txZXBDJdkGZ44Of1bATtgSTjtMnAT8JHeFksIIdaGMPCplFZ/fFv0zmSaQ2TfgcPk08iQoe/3NDZD77hGQ5IMyZwGY8zlAFrrzwF/YIyZ7nuphBBijfB9nyQpE6djykqpvi3DFIMxOe7CM9/0L3fCv9y5sP35z7qIi847e7WKtYxCLU6EHIYll7VkyaUQQiwX+D4judzCmre5+dJQd2mL9s4961R27ztIOY3HUKlEzMwV2fPYoaFqNNSKh63RoLX+MnC7MeZDddvfAVxkjPmNXhdOCCGGXRD4nLB1MT3Rnv0HSZJEehvWsG2bN/Cqf3f5wve79x3kS9/4HtPHh3HOf7p6YkCNhk7+VT8H+HaD7d9OPxNCiCe8MAhIZGLk48rkhBuueOzgET735RvYtXv/KpdouSQezJyGThoN64CZBtvngA29KY4QQqxtYS6kVK4wVyxRGZJww6I742OjrJ8cd6nQj01z1/0PrXaRFql0ImQ8mCWXnTQaHgQub7D9cmCIalAIIVbPuokCJ2/fyobJcaIBBdwR/eV5Hr/90hfxb5//TIChHKawA2qgdhIR8s+AD2utR4DvptteCLwXuKbH5RJCiDXJUwp8RS4X9i28tBi8IPDZsXUTAIePTvNPP7xjlUvkXLGw5HJIgjtVGWM+pbXeCnwQ+K+4uA3zwEeMMf+tT+UTQog1yfM8kjhhbr7U1XlU02C8jx/lUpnBPPKyCwKfMFj6iBwvjJILA8qVCnfc/cAqlWypKwY8EbKTngaMMX+ktf4wcF666R5jTOOsHkII8QQWBj4nbNnY/ePe2sf9xMr5+SIjI8OzTDWKY6aOzy5rNHiex1VXPpd9Bw6vUsmWmp6Zg9td42UoGw1a6w3AFcCpQA54sdYaAGPM+3peOiGEWKOUUowOSRKmYed7MDo6PI2GOE6aZrncvnUT29NhitV26MgU3J4OT0RD1mjQWv8b4AbcsMQkcBDYils9sQ+QRoMQQog1z/MUeKpplsthEYYBi3Eahm/J5Z8CXwE2A0XgUlyPwx3A23tfNCGEEGLwlFKEvk/Sp5TnvRIG/sLwV2KHL7jThcDHjDEJkAA5Y8xuXIPhuj6UTQghhFgVYeAPbEXCSoVBsBCnwQ5oeW8ncxpioJrK7QAu++V9wCFcj0MmWusrcY2Mc3HDGp80xnw0w3FvA94AnADcA7zdGPOdXpxbCCGEqBUEAfPl4c5eGgT+wtfDGEb6p7jeBoB/Ad6ltX4R8CHAZDmB1vpi4O9woacvxMV4uE5r/XttjvvPwB8D16bHfRf4e631+d2eWwghhKiXy4UksaU4X176p1imWCqvdvEAlsy3iIZtIiQuPsN4+vW1wDdxD+iDwFUZz/EW4DZjzDvT7+/VWp8HvAP4dKMDtNYKuBo3NPKFdPPbtNaXpef7Dys9txBCCNHI2Eiek3dsWba9XIl47NDRVShRM254IqoMplckc0+DMeZGY8zX0q93GWPOw02KPMEYc3PG01yKW4FR6wbgVK31SU2OOQ3Y0eS4Z3d5biGEEKIhpdSyP74/ZNlL086GOBpMoyFTT4PWOgQeBi43xtxd3W6MOdLh9bYD9enB9td8trvJMbX71R63vW6/Ts/dVqlU6utkmFKpu2hxQuqwW1J/3ZH6695aqkNrLeVSiVJpfrWLAsAGv0xYSfAnN1AsFntyzlYxMzI1GowxlTSI0xMu+0o+3//gLMMU1GStkjrsjtRfd6T+ureW6vCUk3LYIVmOufvF/5HpXfdz0bNfMJDrdTKn4bPAm4DXd3G9fbjVD7W21XzW7BjS4+6vO25f3X6dnlsIIYToSC7sKJhyX515/tMonn3uwK7XyU++A3iZ1vr5wE+AJTE2jTGvy3COW4AXsTR65BXAw2nMh0Z2AXvT475fd9wPujy3EEIIITLqpNFwJnB7+vWOus+y9tN8DPih1vqDwBeBZwBvBN5c3UFrfQnwBeC3jDG3GmOs1vpPccsn7wV+jFsxcQHw2k7OLYQQQoiV6yQ19mXdXswYc5vW+tdwAZjeipuoeI0xpnZJ5Big07+rx31ca51Pj9sG3Av8qjHmzg7PLYQQQogVUu0mc2itXw58zRgzHNEsekBrbQGMMVkykfR1tkuxWFxTE4CGkdRhd6T+uiP11z2pw+70of6aPhuzLDj9a2B99Rut9b1a61N6UCghhBBCrCFZGg31LY6T6GwuhBBCCCEeB4YstJUQQgghhlWWHgPL8nH94YhqMRhZ5j2smIzjdU/qsDtSf92R+uue1GF3Bll/WSZCJrj4CNWJkJfhslwuiVdpjHlhPwoohBBCiOGQpafhf9Z9f30/CiKEEEKI4da2p0EIIYQQAmQipBBCCCEykkaDEEIIITKRRoMQQgghMpFGgxBCCCEykUaDEEIIITKRcNAtVBNbCSGEEE8kzRI6Sk+DEEIIITKROA1CCCGEyER6GoQQQgiRiTQahBBCCJGJNBqEEEIIkYmsnlglWusrgeuAc4F9wCeNMR9d3VINJ6311cCvA+fgUpXfBXzAGHND3X7PAD4GPA04CnweeLcxJh5ogYec1vr5wHeBh4wxZ9Vsl/prQmu9GXg/8BJgE7AXuM4Y85mafaT+mtBae8C7gd8CTgQOAl8D3mmMma3ZT+oQ0Fo/B/hD4ELgFOBaY8wH6vZpW1da6+3AJ4Ar0k3fAt5kjDmw0rJJT8Mq0FpfDPwd8G3cP4r3AtdprX9vFYs1zJ4P/BUuLfslwA+Bb2itL63uoLU+GfcgNMDTgd8Hfhf44MBLO8S01ifgMtd+p2671F8TWutx4PvAWcArAA28Eri3Zh+pv9b+EHgr8Hbci9JrgauAhRclqcMlxoF7gLcB++s/zFJXaUPtG8DpwOXAC4EnAV/TWjdcTpmF9DSsjrcAtxlj3pl+f6/W+jzgHcCnV69Yw8kY8+K6TW/TWl+B6324Jd32+8A08B+NMQlwt9b6ROBPtNbvr32beaJKbyLXA58CRoCzaz6W+mvuamAM+GVjTCndtqtuH6m/1i4FvmuM+Ur6/S6t9V/jXgiqpA5Txphv4XoF0Fp/uMEuWerql3C9EOcYY0x6rlfjemqfC9y0krJJT8PquBS4oW7bDcCpWuuTVqE8a0r68JsEam8ilwLfSf8HqroBd7O/aIDFG2bXAhZodBOS+mvupcAPgI9prfdpre/TWv+p1nqsZh+pv9Z+AFyqtT4fQGt9BnAl8M2afaQOs8tSV5fihiBNdQdjzN3AbuDZK72wNBpWx3aWdzntr/lMtPYuYD3wlzXbpE5b0FpfBvwe8GpjTKPgLFJ/zZ2J60qfBH4F12X8G8BnavaR+mvtI8B/B27XWleAB4GbcQ3ZKqnD7LLUVaN9qvutuD6l0SDWFK3163GNhquMMbtXuzxrQTqJ73rgNcaYRjcR0ZoHHMHV34+NMV/HDTG+Umu9cXWLtmZcBbweeA2uy/xlwIuBD7Q6SAwfmdOwOvYBJ9Rt21bzmWhAa/1W4I+BXzXG3Fj3sdRpc08BduAmj1a3eYDSWke4Ge1Sf83tA3YZYyo12+5O/z4V16CQ+mvtI8AnjDFfTL//mdZ6FPirdAx+HqnDTmSpq324eQ31ttFFfUpPw+q4BXhR3bYrgIfl7bkxrfX7gD8CrmzQYABXp5en8x2qrgDmgDsGUMRhdhvwVNxKneqfTwOPpl9/E6m/Vm4GztJa175kVVtfu9K/pf5aKwBJ3bYYt4S6OpNf6jC7LHV1C3C61nphwrPW+snAybg5JisiuSdWgdb63+CWDf4J8EXgGbib+JuNMbJ6oo7W+uO45USvAP6l5qOiMWYq3edk3Nvfl3HLuM4EPgd8xhjzjoEWeA3QWr8XeFU1ToPUX3Na6wuAW3FLVT+KGw/+DHCLMea3032k/lrQWv8P4Jdx82ruwDW6/hy42xjzK+k+UoepdJlvNYbKt4C/BT4LzBhjdmapq7RBcRsQAW/ENc4+BVSAZzWZ29SW9DSsAmPMbcCv4f4nuhN4H3CNNBia+gPcEsGv4rrVqn8+Ud3BGPMobh3yucBPcJMk/xK4ZtCFXYuk/pozxtyJm+l/EfCvuJvzV3HL3qr7SP219ibgC7hhivtwja4bgN+u7iB1uMTFuMbVHbhG6hvSrz8L2eoqXVnxy8AjwD/i4jo8CLxkpQ0GkJ4GIYQQQmQkPQ1CCCGEyEQaDUIIIYTIRBoNQgghhMhEGg1CCCGEyEQaDUIIIYTIRBoNQgghhMhEGg1CCCGEyEQaDUIIIYTIRBoNQjwBaK0/r7VulLNDrDKt9bjWek8aXj7rMX+htf5IP8slRCPSaBBiFWitb9Raf35Qx/XbsJZrjXg78OM0vHxW7wN+T2t9Rp/KJERD0mgQQjSktc49Ea7ZjW7Lq7UeweWw+ItOjjPG7MHlE3h9N9cXolNB+12EEL2UvpG/IP26mrDnMlwq2/cDrwa2ADuBDxhj/ner44wxN2mtL8clqzkf8HGJla42xtzaQbluwiW02Qv8Di4r3gnpZ2/EJc05DZdS+/PAh40xUYuf573ATmPM79Rc493A7xhjTmt1zXT7TuDh9Lo54BvA640xMy1+hmcDH07rAeDnwNuMMf9Qs88b0nOeCUwBNxtjXqq1DmlR/93UUbPy4tIZjwLfqbnG/wJe2WT/zxhjXpd+/VXgOuCtLc4vRE9JT4MQg/cHwM3A/8FlsNuOS5V+HfBa4D8DTwGuB67XWr+gzXEA48CfAb8APAt4ALhBa72pw7K9HPfAfAFwOSyk0X4r8E5cVr0/wKUq/6MM5VrRNVNXARuB5wH/Hpex7+3NTqK1DoCvAz8Cnpb+eS8wV7PPH+MaFX8GPBX30L49/bhd/Tctb4Y6aua5wB11DYs3sliPh4Cra75/c81+P8I1sM5tcw0hekZ6GoQYMGPMlNa6DBSNMfsBtNZjuPTBbzbGfDnd9bp0ctw1wD82Oq7mnF+t/V5r/TrgpbiH4v/qoHj7cG/zSU253gb8ujHmhnSfh9Ieg08C1zYrl9Z6Rdes8bAxpvqQvE9r/SXgl4Brm5xnAtgAfN0Y80C6rfo3WutC+rNca4z57zXH3Z6l/puVN0sdtfjZTwf21G4wxhxJz7sV2Az8sP73ndqd/n0GcG+LawjRM9JoEGI4nIXrgv9+3fb/h3t7bUlrfTpuctwvAFtxvYhjwKkdluMndQ/v83Dd51/RWtua7T4worXeYow52OE12l2z6s667/cCL2p2EmPMUa31Z4F/0Fr/E67uvmqMMeku5wEj1AwF1Oik/ntZR6O4IZJGLgAs8LMmn8/XnEOIgZDhCSEeH74BnIIbU38mcCFwAPcg7MRs3ffVe8TL0nNW/zwVOBs40uJcCW7Mv1aY4ZpV5brvLW3uWcaY1wJPB76L6/q/S2v9u62OWYFe1tFB3BBMIxcADxljjjf5vHpct402ITKTngYhVkcZ9yZatRMoAc8B7qrZ/ty67+uPI5238GTgyuqEP631Sbgeh27djXujPcMY860W+y0rF67RsqNu29N6UKaWjDF34erso1rrTwOvw61OuAf3s7wQ+GndYVnrv5GsddTI7cB/avJZu2GHpwIxcEeH1xRixaTRIMTqeAi4TGtdncE/hRv/fr/W+iCua/4q4CUsnRzY6LijuLfN12qtHwQ2AX8CFLstpDFmRmt9HW583wI34u4bTwUuMsZUJyY2KteNwJ9rrV+Ge7BdBfwicKzbcjWitT4LN5Hx73GrF3ak17u95mf5CPBerXUR1xsximtsfUhrnaX+l+mgjhr5NvARrfXJxphH6z7zgS1a69OAvcaY+p6X5wE/MMZMtyqfEL0kwxNCrI6P4GbG34l74F+Km3D3GeDjuLfbVwGvMsb8Y6vj0vH1l+GWEP4Ut9Tv47gJe10zxrwfeAvugXwn8APcLP5dbX6e/wl8Kv3zY+BkXMOoX2ZxwwF/A9wPfAW3iqP2Tf5aXD2/CVfH32Gx9yNL/TeUsY4aHXcvcBNumWe9vwTW4ZZ4LplVqrVWuGWZHcV3EKJbylrbfi8hhBB9obX+RVxD5yxjTKbeIa31y3ENoAuNMXE/yydELelpEEKIVWSMuRn4Y9wchqzywGukwSAGTXoahBBCCJGJ9DQIIYQQIhNpNAghhBAiE2k0CCGEECITaTQIIYQQIhNpNAghhBAiE2k0CCGEECITaTQIIYQQIpP/D38wM70D4la5AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "performance_profile.make_distribution_plot(dict_algorithm, algorithms, reps, xlabel, dic, color)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we just load the test result which should be in the form of `dict_algorithm`.\n",
    "\n",
    "It is dictionary with the names of algorithms as keys, and numpy arraies as values.\n",
    "\n",
    "Each numpy array contains 2 dimensions: the number of the random seed and different experiment scenarios like different time periods or different datasets.\n",
    "\n",
    "The value in the numpy array indicates a sepcific financial indicator's normalized scores under a specific run(fixed seed and scenario).\n",
    "\n",
    "The visualization shows the distributions of the scores for different algorithms. You can specify the colors or the names of the algorithms at will."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from copyreg import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats\n",
    "from rliable import library as rly\n",
    "from rliable import metrics\n",
    "from rliable import plot_utils\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style(\"white\")\n",
    "import matplotlib.patches as mpatches\n",
    "import collections\n",
    "import os\n",
    "load_dict = np.load('visualization_data/rank.npy',allow_pickle=True).item()\n",
    "from visualization.rank.rank import subsample_scores_mat,get_rank_matrix,make_rank_plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using algorithms: ['A2C', 'DeepTrader', 'PPO', 'EIIE', 'SAC', 'IMIT', 'SARL', 'AlphaMix+']\n",
      "Using algorithms: ['A2C', 'DeepTrader', 'PPO', 'EIIE', 'SAC', 'IMIT', 'SARL', 'AlphaMix+']\n",
      "Using algorithms: ['A2C', 'DeepTrader', 'PPO', 'EIIE', 'SAC', 'IMIT', 'SARL', 'AlphaMix+']\n",
      "Using algorithms: ['A2C', 'DeepTrader', 'PPO', 'EIIE', 'SAC', 'IMIT', 'SARL', 'AlphaMix+']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sunshuo/qml/TradeMaster/visualization/rank/rank.py:123: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(yticks, size='large')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "algorithms= ['A2C','DeepTrader','PPO',\"EIIE\",'SAC',\"IMIT\",'SARL',\"AlphaMix+\"]\n",
    "indicator_list=['TR','SR','VOL','Entropy']\n",
    "path=\"result/visualization/rank.pdf\"\n",
    "colors=['moccasin','aquamarine','#dbc2ec','orchid','lightskyblue','pink','bisque',\"lightcoral\"]\n",
    "make_rank_plot(load_dict, algorithms, indicator_list, path, colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we show the rank result for 4 indicators.\n",
    "\n",
    "Unlike the dictonary we use for performance profile, here we use multiple financial indicators, and since we are comparing the rank, so no normalization is required.\n",
    "\n",
    "The plot shows that the possibility that one algorithms could rank over all the seeds and scenarios."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PRUDEX-Compass\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div align=\"center\">\n",
    "  <img src=\"result/visualization/compass.svg\" width = 400 height = 400 />\n",
    "</div>\n",
    "\n",
    "if the graph does not show, please refer [here](https://github.com/ai-gamer/PRUDEX-Compass/blob/main/Compass/pictures/FInal_compass.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we show what the compass looks like.\n",
    "\n",
    "For more information to generate such graph, please refer [here](https://github.com/ai-gamer/PRUDEX-Compass)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"center\">\n",
    "    <tr>\n",
    "        <td ><center><img src=\"result/visualization/pride/A2C.svg\" width = 220 height = 220 />   </center></td>\n",
    "        <td ><center><img src=\"result/visualization/pride/PPO.svg\" width = 220 height = 220 /> </center></td>\n",
    "        <td ><center><img src=\"result/visualization/pride/SAC.svg\" width = 220 height = 220 /> </center></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "     <td align=\"center\"><center>(a) A2C</center></td><td align=\"center\"><center>(b) PPO</center></td>      <td align=\"center\"><center>(c) SAC</center></td>                   \n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td ><center><img src=\"result/visualization/pride/SARL.svg\" width = 220 height = 220 /> </center></td>\n",
    "        <td ><center><img src=\"result/visualization/pride/DeepTrader.svg\" width = 220 height = 220 /> </center></td>\n",
    "        <td ><center><img src=\"result/visualization/pride/AlphaMix.svg\" width = 220 height = 220 />  </center></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "     <td align=\"center\"><center>(d) SARL</center></td><td align=\"center\"><center>(e) DeepTrader</center></td>      <td align=\"center\"><center>(f) AlphaMix+</center></td>                   \n",
    "    </tr>\n",
    "</table>\n",
    "\n",
    "\n",
    "<div STYLE=\"page-break-after: always;\"></div>\n",
    "\n",
    "if the graph does not show, please refer [here](https://github.com/ai-gamer/PRUDEX-Compass/blob/main/Compass/pictures/octagon.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we show what the PRIDE Star looks like.\n",
    "\n",
    "For more information to generate such graph, please refer [here](https://github.com/ai-gamer/PRUDEX-Compass)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.13 ('TradeMaster')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  },
  "orig_nbformat": 4,
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